Most Common Python Mistakes Junior Data Engineers Make (and How to Fix Them)

Python is the backbone of modern data engineering. As one of the most versatile and in-demand programming languages, Python is a cornerstone for solving data challenges and building efficient pipelines. You might feel confident after a few Python classes or scripts, but day-to-day data engineering will quickly reveal gaps in your Python mastery. Many junior data engineers think they know Python—until their “working” code runs on real data and suddenly crawls to a halt or breaks in new and mysterious ways.

The truth is, writing production-grade Python is very different from writing toy scripts. Maybe you’ve seen a simple ETL script blow up when scaling to millions of rows. Or perhaps an Airflow job silently failed and you had no idea until someone noticed the dashboard was wrong. These kinds of silent, subtle Python mistakes are common – and they separate intern-level coders from engineers who can be trusted with reliable pipelines (and land six-figure offers). The good news? Once you learn to spot and fix these issues, you’ll start writing cleaner, faster, and more robust code that just works – even under big data and big company pressures.

New to Python? If you’re just getting started, first walk through our step-by-step guide here:
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Key Takeaways

• 10 Common Python Pitfalls in Data Engineering: Real examples of mistakes that frequently trip up junior data engineers in pipelines and ETL jobs.
• Why These Mistakes Matter: Understanding how these errors lead to slow pipelines, bugs, outages, wrong dashboards, or failed interviews – and how hiring managers immediately spot these red flags.
• Bad vs. Good Code Examples: Each mistake is paired with a “bad” code snippet and a “good” refactored version, so you can see exactly how to debug and improve your own code.
• Practical Fixes for Production-Ready Code: Concrete tips to refactor loop-heavy code, handle data types safely, add logging, manage dependencies, and write cloud-friendly scripts.
• Accelerate Your Career: How mastering these details fast-tracks you from an entry-level coder to a confident data engineer who builds reliable pipelines – and gets ready for higher-paying roles and technical interviews.

Mistake #1: Treating Python Like SQL (and Writing Slow, Loop-Heavy Code)

What it looks like: You write Python code as if you were writing SQL or Excel macros – lots of nested loops and row-by-row processing. For example, you might iterate over a list of records with multiple for loops to filter or aggregate data, where a more Pythonic approach exists. You treat Python lists like tables, using loops to find matches or compute sums, resulting in very slow code on large datasets.

Why it’s a problem: In Python, explicit loops over large datasets are notoriously slow. If you loop through millions of records with nested loops (the way you might approach a SQL JOIN or a GROUP BY manually), your pipeline will grind to a halt. This “row-at-a-time” mindset misses Python’s strengths in vectorized operations and built-in functions. It leads to jobs that take hours instead of minutes and can cause SLA misses or timeouts in production. In an interview, a hiring manager will instantly notice if you write a heavy triple-nested loop to do something that could be done with a set operation or a Pandas grouping – it signals that you haven’t yet learned to “think in Python.”

Bad Code Example: A loop-heavy approach to filter and transform data, doing work element by element:

numbers = [1, 2, 3, 4, 5, 6]
even_squares = []
for num in numbers:
    if num % 2 == 0:            # check if even
        square = num * num      # compute square
        even_squares.append(square)
print(even_squares)  # Output: [4, 16, 36]

In this contrived example, we loop through a list to filter even numbers and compute squares. It works, but for large lists (think millions of entries), this loop will be painfully slow.

Good Code Example: Leverage Python’s list comprehensions to filter and transform in one expression, or use vectorized operations (like NumPy or Pandas) for large datasets:

numbers = [1, 2, 3, 4, 5, 6]
even_squares = [num * num for num in numbers if num % 2 == 0]
print(even_squares)  # Output: [4, 16, 36]

This one-liner replaces the entire loop. It’s not just more concise – in Python, list comprehensions and built-in functions are often optimized in C and run much faster than equivalent Python loops. In data engineering, you’d apply the same idea to DataFrames: for example, using a Pandas vectorized operation or .apply() instead of iterating row by row.

How to fix/avoid it: Embrace Pythonic constructs. Whenever you catch yourself writing a for loop to process each item, ask if there’s a built-in function or list/dict comprehension that can do the job. For filtering and transforming sequences, use comprehensions or functions like filter(), map(), or Pandas operations. For aggregations or membership checks, use data structures like sets, dictionaries, or library functions. Example: Don’t write a double loop to find common elements between two lists – use a set intersection. Not only will your code run faster, it will also be shorter and clearer. Always remember: Python thrives with vectorized, set-based operations. Write Python, not pseudo-SQL!

Mistake #2: Ignoring Data Types and Mutability (Subtle Bugs in Lists and Dicts)

What it looks like: You treat Python variables and data structures without much thought to their types or mutability. Common scenarios include: using a list where a set would be more appropriate (or vice-versa), modifying a list or dictionary in one place and being surprised it changed somewhere else, or using a mutable default argument in a function and encountering weird behavior. For instance, you might write a function like def add_item(item, bucket=[]): ... and wonder why that default list keeps growing across calls. Or you assign one list to another (e.g. list2 = list1) expecting a copy, but actually just create a second reference to the same list.

Why it’s a problem: Python’s dynamic typing and object mutability can lead to silent, subtle bugs that are hard to catch. If you’re not careful, you’ll introduce logic errors: e.g. modifying a list that other parts of your code are still using, causing those parts to see unexpected changes. Using mutable default function arguments is a classic Python bug – it can lead to “state” persisting between function calls when you didn’t intend it. These issues might not throw exceptions; instead, they produce wrong results that are tricky to debug (the worst kind of bug for a data pipeline). In a data engineering context, this could mean your transformation function accumulates data from previous runs or a helper function inadvertently alters a dictionary that another thread is using. Interviewers often probe your understanding of Python’s data model with questions about this (for example, asking what happens with mutable default params or how Python handles variable assignment). A shaky answer or a buggy code sample here is a red flag.

Bad Code Example: Using a mutable object as a default function argument – a classic source of subtle bugs:

def add_user(user, user_list=[]):
    user_list.append(user)
    return user_list

print(add_user("Alice"))  # Expected ['Alice']
print(add_user("Bob"))    # Expected ['Bob'], but actually gets ['Alice', 'Bob']

You might expect the second call to start with a fresh list, but because user_list is a mutable default, it remembers state across calls. The output will be ['Alice'] then ['Alice', 'Bob'] – clearly not what we intended, and potentially disastrous if this function is part of building a list of items within an ETL job. In Python, the default list is evaluated once when the function is defined, and the same list object is used for every call.

Good Code Example: Use None as the default and create a new list inside the function, or otherwise ensure each call gets a fresh object:

def add_user(user, user_list=None):
    if user_list is None:
        user_list = []
    user_list.append(user)
    return user_list

print(add_user("Alice"))  # ['Alice']
print(add_user("Bob"))    # ['Bob'] – now works independently per call

Also, be mindful of assignment vs. copying with collections. If you do list2 = list1, no new list is created – list2 is just another name for the same list in memory. Any change to one is a change to both. To truly copy, use list1.copy() or slicing (list2 = list1[:]). For nested structures or more complex cases, the copy module’s deepcopy may be needed. This applies to dicts and other containers too. Junior engineers often get bitten by this when they, say, pass a dict into a function which modifies it in place – the changes persist outside the function (because the reference is shared), causing bizarre side-effects.

How to fix/avoid it: Develop the habit of thinking about data types and mutability when writing Python code. Some tips:

• Prefer immutable types (tuples, frozensets) for data that shouldn’t change. If a function doesn’t need to modify a list, maybe take a tuple instead to signal that.
• When using mutable structures (lists, dicts, sets), be explicit when you need a copy. Use .copy() or constructors (new_list = list(old_list)) to create independent objects as needed.
• Never use a mutable object as a default parameter. Use None and inside the function initialize a new list or dict. This one rule alone will save you countless hours of debugging.
• Use tools like type hints or linters – they can warn about these common traps (some linters will flag mutable defaults). And in interviews, mentioning how you avoid these pitfalls – e.g. “I always avoid mutable default args” – will score you points.

Understanding Python’s data model (how names and objects work) is key to writing robust code. It prevents those “I have no idea why this list is now twice as long” moments that plague many beginners.

Mistake #3: Swallowing Exceptions Instead of Logging Them

What it looks like: Your code has broad try/except blocks that catch errors – and then do nothing with them (or merely print a vague message). For example, you might wrap a whole ETL step in try: ... except Exception as e: pass to “prevent” crashes, or do except Exception as e: print("Error occurred") without actually logging the error or stack trace. The result is that when something goes wrong, the code fails silently or with minimal info.

Why it’s a problem: Silencing exceptions is one of the most dangerous things you can do in a data pipeline. If your job encounters a bad record or a database connection issue and you’ve swallowed the exception, the pipeline might continue running with bad data or partial results. Or it might fail and you’ll have no clue where or why. This turns debugging into a nightmare – you end up searching through millions of rows or re-running code with prints to find the issue. In production, silent failures can lead to wrong business metrics or reports (since errors were never surfaced) or missed SLAs. From an interview perspective, using pass on exceptions is a hallmark of an inexperienced engineer. Hiring managers expect you to at least log errors. In fact, a common interview question is, “What’s your approach to error handling in your pipelines?” – if your answer is, “I just wrap it in a try/except and ignore the error,” that’s an instant red flag. They want to hear that you log exceptions and maybe even have alerting or retry logic.

Bad Code Example: A broad try/except that catches everything and does nothing useful – effectively hiding errors:

try:
    result = process_data(file_path)
except Exception:
    # Ignoring all errors - pipeline continues or fails silently
    pass

print("Job completed")  # It prints "Job completed" even if process_data failed!

In this snippet, if process_data raises any error (be it a file not found, a JSON decode error, etc.), the except will catch it and do nothing. The code will then print “Job completed” as if all went fine. This is terrifying in a data engineering context – your pipeline could be silently failing or producing incorrect results, and you’d be none the wiser. Unfortunately, this pattern is more common among juniors than you’d think (often done to avoid “ugly error messages”). It’s better to let it crash than to hide the error entirely.

Good Code Example: Catch only what you expect, log the exception with details, and if appropriate, re-raise or handle it:

import logging

try:
    result = process_data(file_path)
    logging.info("Data processed successfully")
except Exception as e:
    logging.error(f"Failed to process {file_path}: {e}", exc_info=True)
    raise  # Optionally re-throw after logging

Here we import Python’s built-in logging module and use it to record an error message, including the exception details (exc_info=True will include the stack trace in the log). By logging and then re-raising, the error won’t go unnoticed – it will either be caught by an outer system (like Airflow, which will mark the task as failed) or at least leave a trace in your logs for later debugging. Never use a bare except: without at least logging the error. As a rule, don’t catch an exception unless you can handle it or need to add context. Letting your program crash and alerting you is much better than a silent failure.

How to fix/avoid it: Always handle exceptions transparently. This means:

• Use the logging module to report errors. Configure logging at the start of your script or pipeline (set a level, and optionally a file to write logs). For example, logging.basicConfig(level=logging.INFO) at minimum, and use logging.error() or logging.exception() in your except blocks instead of print. Logged exceptions will show up with timestamps and stack traces, which are invaluable for troubleshooting.
• Be specific with exceptions. Catch the exact exceptions you expect (e.g., except FileNotFoundError:) and handle those. Don’t do a blanket except Exception unless you really intend to catch everything. Specific exception handling ensures you don’t accidentally mask bugs you weren’t expecting.
• If you catch an exception and can’t fully handle it, consider re-raising it after logging. This way, upstream systems know the task failed. For example, in Airflow, if your code raises an exception, the task is marked failed and you can get notified. If you swallow it, Airflow thinks the task succeeded (when it actually didn’t complete correctly).
• Use monitoring/alerting (more on that later in Mistake #9). In practice, you want failures to trigger alerts – which can only happen if failures aren’t hidden.

Remember, exceptions are your friends in development – they tell you something went wrong. Don’t muzzle your friends! Log them, understand them, and only suppress them if you have a very good reason (and even then, log that it happened). This approach shows that you write transparent code – a trait of a trustworthy data engineer.

Mistake #4: Writing One Giant Script Instead of Small, Testable Functions

What it looks like: Your entire pipeline or job is written as one monolithic script or notebook: a hundred (or thousand) lines of sequential code, possibly in a main() or just top-level, without any modular structure. There are no functions except maybe the default if __name__ == "__main__": wrapper. Configuration, logic, and processing steps are all interwoven. For example, you open connections, transform data, and write output all in one sprawling block of code. There’s little to no reuse, and if you wanted to unit test a part of the logic, you’d have to invoke the entire script.

Why it’s a problem: Such “giant script” coding might work for a one-off task, but in production it becomes unmaintainable very quickly. It’s hard to debug (you can’t easily isolate where something went wrong), hard to extend (touch one part and you might break another), and nearly impossible to unit test. If every change requires running the whole script on a full dataset, you’ll dread updates. This also makes onboarding others difficult – new team members face a wall of code with no clear entry points or abstractions. Career-wise, writing code this way keeps you at the novice level. Experienced engineers (and interviewers) favor well-structured code. In fact, interview coding challenges often reward candidates who naturally break the problem into helper functions. If you present one giant, convoluted script, it signals you haven’t learned to design and organize code. As one author aptly put it, messy, unstructured code is like trying to run a marathon with a backpack full of rocks – it will slow you down and turn your projects into bug-filled, hard-to-update nightmares. And you can bet that experienced interviewers can sense this chaos in your code even if it “works”.

Bad Code Example: A simplified illustration – doing everything in one place:

# Bad: Everything in one script, no functions
import pandas as pd

# 1. Read input
df = pd.read_csv("users.csv")

# 2. Transform data
df['full_name'] = df['first_name'] + " " + df['last_name']
df = df[df['active'] == True]

# 3. Load to target
df.to_excel("active_users.xlsx", index=False)

print("Job done!")

This script reads a CSV, creates a column, filters rows, and writes to Excel, all in sequence. It seems harmless for this tiny task, but imagine a real pipeline with 10–20 steps like this, possibly involving multiple data sources and complex transforms – the code would balloon in length and complexity. There’s no easy way to reuse the transformation logic elsewhere or test it in isolation. If something fails in step 7 of 15, you have to rerun the whole thing after fixing. Logging and error handling often get intermingled awkwardly in such scripts as well.

Good Code Example: Refactor the logic into small, focused functions and call them in a main workflow:

# Good: Modular approach with functions
import pandas as pd

def extract_users(csv_path):
    return pd.read_csv(csv_path)

def transform_users(df):
    df['full_name'] = df['first_name'] + " " + df['last_name']
    return df[df['active'] == True]

def load_users(df, out_path):
    df.to_excel(out_path, index=False)

if __name__ == "__main__":
    users_df = extract_users("users.csv")
    active_users_df = transform_users(users_df)
    load_users(active_users_df, "active_users.xlsx")
    print("Job done!")

Now each step is in its own function: extraction, transformation, loading. This has immediate benefits: you can unit test transform_users by feeding it a small DataFrame and checking the output (no need to involve the file system). If a bug appears in the transform logic, you know to look inside that function. The code is also self-documenting – it’s clear what the high-level steps are.

In an interview, if asked to design a pipeline or solve a problem, breaking your solution into functions shows you have clean coding habits. It demonstrates foresight: you’re writing code that could scale or be maintained by others. It’s the difference between an intern script and engineer-quality code.

How to fix/avoid it: Adopt a modular mindset. Some tips:

• Think in functions (or classes): Whenever you start a new task, ask, “Can I encapsulate this in a function?” Ideally, each function does one thing (single responsibility principle). For data pipelines, common functions might be extract_<resource>, transform_<something>, load_<target> as seen above.
• Use meaningful abstractions: If certain code repeats or can be generalized, wrap it in a function. For example, if you notice you’re doing similar cleanup on multiple DataFrames, create a utility function for it. This not only avoids repetition (DRY principle), but if requirements change, you update one place instead of 5.
• Easier testing: Functions make unit testing feasible. Without functions, you resort to running the whole pipeline to test anything – which is slow and costly. With functions, you can quickly test bits of logic with sample inputs. This is a huge boost to your confidence in the code.
• Better debugging: If your pipeline errors out, a structured codebase helps pinpoint where it likely happened (e.g., in the transform_users step vs “somewhere in the 300 lines of sequential code”). You can add logging inside specific functions without cluttering the main flow.
• Refactoring-friendly: As requirements evolve (they always do), you’ll find it much easier to modify or extend a modular codebase. Add a new transform step? Just write a new function and call it in the pipeline. Switch from CSV to database? Change extract_users implementation, leaving others untouched.

In short, don’t dump spaghetti code in your pipeline scripts. Clean code isn’t just academic – it directly impacts your ability to deliver reliable data. Plus, when you show such code to a senior engineer (or interviewer), they’ll nod in approval seeing that you structure your code for maintainability and clarity. This is how you level up from just hacking things together to engineering solutions.

Mistake #5: Messy pandas Code and Chained Indexing on DataFrames

What it looks like: Your Pandas code is a tangle of poorly-thought-out operations – maybe a dozen chained method calls in one monster line, or multiple DataFrame slices chained together. A very common specific mistake is chained indexing, like df[df['col'] > 0]['other_col'] = 5. You might also be inadvertently creating copies of DataFrames and then modifying the wrong one. These lead to the infamous SettingWithCopyWarning or, worse, silently not doing what you expect. In general, “messy” pandas code could mean you’re using a lot of sequential operations where a vectorized approach would do, or using iterrows() to loop through DataFrame rows, or simply not handling missing data and index alignment properly – resulting in confusing bugs.

Why it’s a problem: Pandas is powerful but has its pitfalls. Chained indexing (doing df[...]... in multiple steps) can create a view vs copy ambiguity. Sometimes you end up modifying a copy of the data instead of the original DataFrame – meaning your change doesn’t stick, and you might not even get an error, just a warning. This is a common gotcha that can lead to incorrect data in your pipeline (e.g., you thought you updated 1000 rows, but those updates never actually took effect in the main DataFrame). Additionally, writing very convoluted pandas code reduces readability and can introduce performance issues (e.g., if you could have done an operation in one vectorized call but instead did it in three separate ones or, heaven forbid, in a Python loop with iterrows()). Interviewers (especially those who use pandas often) know these pitfalls. If they see a candidate chaining DataFrame indexing in a way that triggers SettingWithCopyWarning, it signals lack of experience in robust DataFrame manipulation. Clean, careful pandas code is a hallmark of a data engineer who has moved beyond beginner level.

Bad Code Example: Chained indexing to filter and then assign, which can lead to SettingWithCopyWarning and unreliable results:

# Bad: Chained indexing example
import pandas as pd
df = pd.DataFrame({"A": [10, 20, 30], "B": [1, 2, 3]})
# Try to set B = 0 for rows where A > 15 using chained indexing
df[df['A'] > 15]['B'] = 0   # This may not do what you expect!

Here, df[df['A'] > 15] creates a temporary DataFrame (copy) of the rows where A > 15. Then we attempt to set 'B' = 0 on that temporary object. Pandas will usually warn: “A value is trying to be set on a copy of a slice from a DataFrame” (the SettingWithCopyWarning). There’s a chance the assignment doesn’t affect the original df at all. In this example, after running that line, you might find df.loc[1,'B'] still equals 2 instead of 0 – meaning our operation failed silently. This kind of bug can be devastating in data pipelines, because you thought you updated some values but actually didn’t – leading to incorrect data downstream.

Good Code Example: Use .loc for chained operations, or break it into steps using a copy if needed:

# Good: Use .loc for safe chained assignment
df.loc[df['A'] > 15, 'B'] = 0

This one line does the filtering and assignment in one go, on the original DataFrame, and is the recommended way to set values based on a condition. No ambiguous chaining, no warning. Alternatively, if you really need to work with a subset first, explicitly copy it:

subset = df[df['A'] > 15].copy()
subset['B'] = 0
# (then merge subset back or use it as needed)

By calling .copy(), you make it clear you’re working on a separate DataFrame, and Pandas won’t warn about it.

Aside from chained indexing, another messy pandas pattern is overusing loops. If you find yourself writing for index, row in df.iterrows(): ..., it’s usually a sign you’re not using pandas optimally. Nearly anything in a loop can be done with vectorized operations or at least df.apply(), which are much faster. For example, rather than looping to compute a new column, do df['new_col'] = df['col1'] + df['col2'] or use .apply with a lambda if it’s more complex.

How to fix/avoid it: Develop a set of pandas best practices:

• Avoid chained indexing: If you see df[...]...[...], stop and use .loc or .iloc properly. This removes any ambiguity – you’re explicitly saying “on df, for these rows and cols, do this.” Pandas documentation itself advises against chained indexing.
• Heed warnings: If you do get a SettingWithCopyWarning, don’t ignore it. It’s telling you that you might have a bug. Refactor that part with the proper approach (usually using .loc assignment as shown).
• Write clear transformation steps: It’s often better to break a complex chain of operations into multiple steps with intermediate variables, especially if it improves readability. For example, it’s fine to do active_users = df[df['active'] == True].copy() on one line and then active_users['full_name'] = ... on the next. This way, you know active_users is a separate DataFrame, and your code is easier to read than one gigantic expression chaining five methods.
• Leverage vectorized ops: Always ask “Can pandas do this for me without looping?” Use built-in functions like df.sum(), df.groupby(), df.merge(), etc., which are highly optimized. If you need to do a custom row-wise operation, consider df.apply rather than manual loops. The difference can be huge – vectorized operations in pandas are implemented in C and can handle large datasets efficiently, whereas Python loops will slow to a crawl for big DataFrames.
• Be mindful of copies vs. views: Use .copy() when creating subsets you plan to modify. This makes your intent explicit and avoids those tricky situations where modifying a view either doesn’t work or (even scarier) works on the wrong data.

Writing clean pandas code is a skill that comes with practice. If you adhere to these guidelines, your data manipulation code will be more robust and easier to maintain. And when an interviewer asks you to do something with a DataFrame, you’ll impress them by avoiding the common pandas pitfalls that trip up novices.

How to Practice Writing Data-Engineer-Grade Python

Learning about mistakes is one thing – actively practicing better habits is where you really level up. Here are some tips to turn these lessons into muscle memory:

1. Rebuild a Small ETL Pipeline: Take a simple data task (e.g. reading a CSV, transforming it, and loading to a database or file) and implement it first in the naive way, then refactor it. For instance, write it as one script with loops and prints, then refactor into functions with logging and more Pythonic constructs. This will highlight the differences for you. Challenge yourself to remove every obvious loop and replace it with a comprehension or pandas operation. Run the pipeline on a sample dataset before and after – did the refactor maintain the same output? How much faster or clearer is it now?

2. Review Your Old Code: If you have past projects or assignments, go back and audit them for the mistakes in this article. Did you use a mutable default somewhere? Are there any try/except: pass blocks? Are you constructing file paths by string concatenation (and could that break in another environment)? Make a checklist (see below) and systematically find and fix these issues. Not only will this improve those projects, it will cement your understanding of why these patterns are harmful.

3. Embrace Code Reviews: If you have access to peers or a mentor, ask them to review some of your code with a focus on these Pythonic best practices. A fresh set of eyes might catch something you missed. If you’re on your own, consider online communities (like Stack Overflow or Reddit’s r/codeReview) – sometimes experienced developers will review a small snippet for you and provide feedback. This can be invaluable.

4. Work on Data Engineer Academy projects/modules: At Data Engineer Academy, we emphasize writing production-grade code from day one. If you’re a student, go through the projects with an eye on these specific issues. For example, our end-to-end project on building a data pipeline in the cloud will force you to use environment variables for credentials and proper logging. Practicing in a realistic project setting, with guidance, is one of the fastest ways to internalize these habits.

5. Use Tools to Enforce Quality: Linting and formatting tools (like flake8, pylint, or black) won’t catch all these mistakes, but they instill good general practices. More specific tools or settings can help too – e.g., enabling warnings as errors in pandas to catch SettingWithCopy, or using mypy to catch type issues that could hint at wrong data structure usage. In a work setting, continuous integration can run tests and linters to prevent bad patterns from creeping in. As a learner, running these tools locally can guide you towards cleaner code.

Finally, here’s a quick mini-checklist you can use before you push any code or mark a project as complete:

• Logging present? (No bare print for important events/errors, and definitely no swallowed exceptions)
• No hard-coded secrets or paths? (Credentials in env variables, configurable paths, constants for magic numbers)
• No obviously inefficient loops? (Using built-ins/pandas instead; any remaining loops are truly necessary)
• Functions and modular structure? (No giant script; code is organized into logical functions or classes)
• Proper error handling? (Specific exceptions caught, with logging; failing fast when needed)
• Environment-specific assumptions removed? (Code should run on a different machine or cloud service with minimal tweaks)

If you can tick all these boxes, you’re well on your way to writing data-engineer-grade Python code consistently.

Mistake #6: Hard-Coding Credentials, Paths, and Magic Numbers

What it looks like: You have database passwords, API keys, file paths, or numeric constants written directly in your code. For instance, DB_PASSWORD = "SuperSecret123" at the top of your script, or file_path = "/Users/yourname/data/input.csv" hard-coded in a function. You might also have “magic numbers” sprinkled around – unexplained constants like threshold = 0.85 or using the number 3 in a loop because you know your data has exactly three partitions, etc. These values are not configurable; they’re literally typed into the source. The code works fine on your machine with your directory structure and your credentials – but only there.

Why it’s a problem: Hard-coding secrets (passwords, API tokens) in code is a serious security risk. If that code ever gets committed to a repository, especially a public one, you can essentially consider those credentials compromised. Even in private repos, it’s a bad practice: it’s easy to accidentally log or expose them, and rotating credentials becomes a nightmare (you’d have to change the code and redeploy just to update a password). Hard-coded file paths make your code unportable – it runs on your laptop, but likely fails in production or on a colleague’s machine because the exact path doesn’t exist. It also often implies you didn’t consider environment differences (like Windows vs Linux file separators, or using relative vs absolute paths). Magic numbers are a maintainability issue: they obfuscate the purpose of a value and make it hard to change. Why 365? Is that days in a year? Workdays? If an interviewer sees a lot of unexplained constants or, worse, sees an API key in your code, they’ll be concerned. In real data engineering teams, we use config files, environment variables, and secret managers – not hard-coded values. So leaving these in code telegraphs that you’re still in a “hacking it together” mindset rather than engineering. Plus, using hard-coded secrets can even get you in trouble legally or professionally if they ever leak.

Bad Code Example: Credentials, paths, and a magic number all hard-coded:

# Bad: Hard-coded credentials and paths
DB_USER = "admin"
DB_PASSWORD = "SuperSecret123"    # sensitive info exposed in code
DATA_FILE = "C:\\Users\\me\\projects\\data\\input.csv"  # works only on my Windows PC

result = transform(data)
if result > 0.85:    # 0.85 as a magic threshold with no explanation
    alert_team()     # Who knows why 0.85? This is a "magic number"

This code has multiple issues: If someone else runs it, they likely don’t have C:\Users\me\... path, so it breaks. The password being in plain text means if this code is checked into version control, it’s there forever in history (even if later removed, it could be snagged). The 0.85 might be some threshold for model accuracy or something, but it’s not clear – and if it needs changing (say to 0.9), you have to hunt it down in code.

Good Code Example: Externalize and parameterize these values:

# Good: Use environment variables or config for secrets/paths, and named constants
import os

DB_USER = os.getenv("DB_USER")            # read from environment
DB_PASSWORD = os.getenv("DB_PASSWORD")    # e.g., set in the environment, not in code
DATA_FILE = os.getenv("DATA_FILE", "data/input.csv")  # default to a relative path

THRESHOLD = 0.85  # at least it's a named constant now, easier to find/tweak
result = transform(data)
if result > THRESHOLD:
    alert_team()

In this improved snippet, we use os.getenv() to fetch credentials and file paths. This means the actual sensitive values can be provided at runtime (for example, in a secure way via your orchestration tool or an environment config), and they won’t live in the source code. If using a config file or secret manager, it would be similar – the key point is the code is no longer tied to a specific secret or path. We also replaced the raw 0.85 with a named constant THRESHOLD (and ideally a comment or config indicating what it is). This way, if someone needs to adjust the threshold, it’s a clearly defined variable, not a mysterious number embedded in logic.

How to fix/avoid it:

• Use Configuration: Separate configuration from code. Read from environment variables (popular for cloud deployments – e.g., your AWS credentials are picked up from env vars by AWS SDKs), use .env files (with a library like python-dotenv), or use config files (YAML/JSON) that your code loads at runtime. Many frameworks and projects have a config module to handle this. The idea is that when you move from dev to prod, you just change a config file or environment settings, not the code.
• Never commit secrets to Git: As a rule, don’t even put secrets in code to begin with. But if you somehow have to test, use a local config and add it to .gitignore. There are also scanners (like git-secrets) that can catch if you accidentally try to commit an AWS key or password – use them! The cost of leaked credentials is huge.
• Parameterize file paths: Instead of absolute paths, consider relative paths or make the paths configurable (as shown by reading an env var with a default). Better yet, when working in cloud environments, abstract away the path concept – e.g., use a config to choose between local vs S3.
• Replace Magic Numbers with Constants or Calculations: If a number has a meaning (like “3 retries” or “0.85 confidence threshold” or “365 days”), define it as a constant with a name (MAX_RETRIES = 3) or derive it (DAYS_IN_YEAR = 365). This makes your code self-documenting and easier to change. If the “magic” value truly should never change, a constant is fine. If it might change, consider moving it to a config. The key is to not have unexplained literals floating around. As one Stack Overflow post succinctly says, magic numbers make code hard to understand and maintain, whereas using named constants improves readability and maintainability.

By eliminating hard-coded values, you make your code flexible, secure, and production-ready. It should run on any machine or environment as long as it’s given the right config. This is crucial for writing code that works not just on your laptop but on servers, in Docker containers, in cloud functions, etc. (We’ll touch more on environment differences in Mistake #10.) And on the job or in interviews, being able to discuss how you manage configuration and secrets is a sign of maturity as an engineer.

Mistake #7: Ignoring Virtual Environments and Dependency Management

What it looks like: You install packages globally on your system and run everything from the same base environment. Maybe you’ve experienced the “it works on my machine” scenario where you have library X version 1.2 installed, but production has 1.5 and your code breaks – because you never isolated or specified your dependencies. Signs of this mistake include: no requirements.txt or pyproject.toml in your project, not using venv or Conda or any environment tool, and perhaps having multiple projects sharing conflicting library versions. For example, you might pip install PyArrow for one project, then another project needed a different version of PyArrow and now one of them is busted. Or you deploy code without listing dependencies, assuming “numpy is everywhere” – only to find out the target environment doesn’t have it.

Why it’s a problem: Not managing dependencies leads to “works on my machine” syndrome and deployment headaches. In data engineering, you often use numerous libraries (pandas, numpy, pyspark, boto3, etc.). If you don’t pin versions, an update to a library could suddenly break your pipeline (maybe pandas changed a function behavior in a new release). If you don’t isolate environments, one project’s requirements can clobber another’s. This is especially problematic when deploying to cloud or different OSes – the environment needs to be replicable. Virtual environments ensure that your project has exactly the libraries it needs, in the correct versions, and nothing more. Ignoring this is a mark of inexperience; it’s something even small projects should do. Interviewers might ask “How do you manage dependencies or ensure reproducibility?” – they want to hear that you use virtual envs or containers. If you answer “I just pip install on my laptop and that’s it,” it won’t inspire confidence.

Bad Example: (It’s not a code snippet per se, but a common scenario) You work on a pipeline and use Pandas 1.3 locally. You deploy your script to a cloud VM which happens to have Pandas 1.1 installed. Your code calls a function that didn’t exist in 1.1, and it crashes at runtime. Or, you have an Airflow server where multiple workflows run – you do a pip install some_package to satisfy one task, and unknowingly downgrade another package that another task needed. Suddenly other pipelines start failing. These things happen when dependencies aren’t clearly isolated and defined. Another bad sign: you have no requirements.txt to describe what libraries and versions your project needs, so setting up a new machine to run it is a manual, error-prone process (“Hmm, I get import errors… let me pip install this… oh now something else is missing…”).

Good Example: Use a virtual environment and pin your requirements:

• Create a virtual env for your project:

$ python3 -m venv venv
$ source venv/bin/activate

• Install specific versions of needed libraries:

$ pip install pandas==1.5.3 numpy==1.21.0 boto3==1.28.0

• Freeze the requirements to a file:

$ pip freeze > requirements.txt

Now requirements.txt lists exactly what your project uses. Anyone (or any server) can recreate this environment by doing pip install -r requirements.txt in a fresh venv. If you’re using Poetry or Conda, the idea is similar – you have a defined environment that can be reproduced. This practice prevents the compatibility issues and “works on my machine” problems because you’re explicit about dependencies. Also, using a virtual env means you’re not polluting your system Python or other projects – each project’s libs stay in its own sandbox.

How to fix/avoid it:

• Always use a virtual environment or container: For Python, venv is lightweight and built-in. Conda is an alternative if you’re managing Python and non-Python dependencies together. The key is isolation. In a company setting, you might even use Docker containers for each job – with a Dockerfile that pip installs specific versions. The principle is the same: isolate and control the environment.
• Pin versions in requirements: Especially for production code, you don’t want “floating” dependencies (where pip install pandas just grabs the latest version differently today than yesterday). Pin to a known good version. This doesn’t mean you never upgrade – it means upgrades are conscious decisions, not accidents. It’s common to see a requirements.txt with package==X.Y.Z for each package. That ensures that if someone else installs it, they get exactly X.Y.Z, not X.Y.(Z+1) which could have breaking changes. Tools like Poetry or Pipenv handle this via lock files.
• Document setup instructions: Make it easy for others (or future you) to set up the project. A simple README that says “run python -m venv venv && source venv/bin/activate && pip install -r requirements.txt” can save hours of frustration. This is often expected in technical interviews if you share code – it should be straightforward to run.
• Beware of dependency conflicts: Sometimes you’ll need a library that itself depends on a specific version of another. If you ignore dependency management, you might get into “dependency hell.” Using pip’s constraint files or tools like Poetry helps resolve compatible versions. As a junior, you’re not expected to know everything about dependency resolution, but you are expected to not ignore the topic entirely. Show that you know to create an environment for your code.

By managing dependencies diligently, you’ll avoid a lot of “mystery bugs” that are not really code issues but environment issues. This is a mark of professionalism. It also ties into the next mistake: performance and memory issues, because sometimes using the wrong version of a library can implicitly affect that too. But the main point: control your environment – don’t let global installs and version mismatches derail your pipeline.

Mistake #8: Not Thinking About Memory and Performance on Large Datasets

What it looks like: You write Python code that works fine on a small test subset, but when pointed at the full dataset (say a 10 million row CSV or a huge JSON log file), it runs out of memory or takes forever. Classic examples: reading an entire huge file into memory with read() instead of processing it in chunks, using normal lists or DataFrames for data that doesn’t fit in RAM, or using algorithms that are O(n^2) on a dataset that’s way too large for that. Another sign is not considering generator patterns – for example, accumulating a massive list of results when you could iterate and yield results one by one. Essentially, the code isn’t scalable: it doesn’t gracefully handle growing data sizes.

Why it’s a problem: Data engineering is large-scale by nature. We often deal with billions of records, or at least files so big you can’t hold it all in memory at once. If you ignore memory and performance, your pipeline might work on toy data but will crash or slow to a crawl in production (when the data volume is real). This can lead to missed deadlines (pipelines not finishing overnight), out-of-memory errors that cause task failures, or even entire machines going down if they swap to death. Performance issues might also incur unnecessary cloud costs if your jobs take 5x longer than they should. In interviews, you may be asked how you’d handle a dataset that’s too large for memory, or to discuss complexity of your approach. A junior who says “I’ll just load it into a list and loop” for a huge dataset will be gently corrected – they’re looking for you to mention generators, streaming, chunk processing, or distributed frameworks when appropriate. Not every data engineering task is “big data,” but you should always at least think about what happens as data grows.

Bad Code Example: Reading a large file in one go and using a list where a generator could be used:

# Bad: Reading entire file into memory and storing lines in a list
with open("huge_log.txt", "r") as f:
    lines = f.readlines()    # loads all lines into a list (might be huge)
for line in lines:
    process(line)

If huge_log.txt is, say, 5 GB, this code will try to load 5 GB of text into RAM at once. Many machines would simply run out of memory. Even if it doesn’t, it’s inefficient – we don’t need all lines at once, just one at a time to process. Similarly, using f.read() for a big file is dangerous for the same reason. Another example is using pandas on a dataset that doesn’t fit into memory – doing pd.read_csv("very_large.csv") without specifying chunksize or without considering a tool like Dask or PySpark if it’s truly big. The code will either crash or start thrashing the system.

Good Code Example: Stream processing using generators or chunking:

# Good: Process file line by line using a generator
def read_file_in_chunks(file_name):
    with open(file_name, "r") as f:
        for line in f:
            yield line

for line in read_file_in_chunks("huge_log.txt"):
    process(line)

This approach never loads the whole file into memory – it reads one line at a time, yielding it to the loop. This is much more memory-efficient In fact, Python’s file object is already an iterator, so we could even simplify to for line in open("huge_log.txt"): which implicitly does the same. The key is: use generators (with yield or generator expressions) for large data streams instead of materializing giant lists. Another example: If using pandas, leverage chunksize parameter of read_csv to process the file in portions rather than all at once. Or use libraries designed for big data (PySpark, Dask) when data truly exceeds one machine’s memory.

For algorithms, consider complexity: a double loop that compares every pair of records is O(n^2) – that’s fine for 100 records (10k comparisons) but impossible for 1e6 records (1e12 comparisons). Always ask, “What’s the size of data this needs to handle?” and choose approaches accordingly.

How to fix/avoid it:

• Use generators and streaming: In Python, if you only need one item at a time, don’t store them all. Generator functions and expressions (like (x*x for x in range(1000000)) which produces one square at a time) are your friends. This applies to file IO, API results, etc. The motto: iterate, don’t accumulate (unless you really need the whole collection at once).
• Process data in chunks: Many libraries have options for this. For pandas, as mentioned, pd.read_csv(..., chunksize=10000) lets you loop through chunks of 10k rows. You can then process each DataFrame chunk and perhaps aggregate results. This prevents memory blow-ups. Yes, it might be a bit more coding vs one line to read everything, but it’s necessary when data is large.
• Be mindful of data structures: Sometimes using a more appropriate structure can save memory and time. For example, if you need to do membership checks on a large collection, using a set is typically faster than a list (average O(1) vs O(n) lookup) and signals that you only care about existence, not order. Likewise, if you have key-value lookups, a dict is the way to go (instead of scanning a list of tuples each time). These choices greatly affect performance on large data.
• Consider the scale early: When writing a piece of code, ask yourself how it will behave if the input grows 10x, 100x, etc. If you realize “Oh, this does 3 passes over the data, and each pass is linear, so 3n – that’s fine,” or “Oops, this does n^2 comparisons; at a million records that won’t fly,” you can adjust your approach. Big-O analysis might sound theoretical, but it directly impacts pipeline feasibility.
• Use the right tools for big data: If your data is truly beyond a single machine’s capacity, a common junior mistake is to try to handle it all in vanilla Python/Pandas anyway. Know when to reach for PySpark or Dask or a database. For instance, rather than aggregating 100 million rows in Python, you might load them to a SQL database or Spark DataFrame and let those optimized systems do the heavy lifting. As a data engineer, being pragmatic about using the proper technology is key. Python is great, but it has limits – and part of writing production-grade code is knowing those limits.

By being conscious of memory and performance, you’ll write pipelines that are robust and scalable. That means fewer 2 AM on-call incidents because a job ran out of memory, and more confidence when saying “Yes, this code can handle next year’s data growth.” Interviewers often favor candidates who demonstrate this foresight, as it shows you can not only solve problems but also handle them at scale.

Mistake #9: No Logging or Monitoring in ETL Jobs

What it looks like: Your data pipeline runs… and when something goes wrong, you have to scramble to figure out what happened because there are no logs or metrics. Perhaps you rely solely on print statements (or no output at all) to know what the code is doing. You’re not tracking start/end times, row counts, or any useful info. There’s no alerting – you find out about an issue only when a downstream user complains (“The dashboard is empty today!”). In code, this mistake manifests as either lack of using the logging module for structured logs, or not integrating with any monitoring system. Maybe your Airflow tasks don’t push any custom logs, or your standalone Python scripts just silently do their thing without recording progress.

Why it’s a problem: Logging and monitoring are the eyes and ears of your pipeline. Without them, you’re effectively flying blind. When an ETL job fails at 3 AM, would you rather wake up and see a clear error message and context in your log… or have nothing, requiring you to rerun and step through with a debugger? Lack of logging means slow resolution of issues and sometimes even not noticing issues. Lack of monitoring (like not checking if data volumes suddenly drop to zero or latency spikes) can mean problems go undetected until they’ve caused significant damage. From a career perspective, an engineer who doesn’t add logging is going to struggle in a production environment – and interviewers know this. They might ask how you’d design monitoring for a pipeline, or simply note whether your take-home assignment code prints anything useful. Using proper logging (with levels INFO, WARN, ERROR) indicates professionalism. Also, many companies ask about how you ensure data quality or pipeline reliability; mentioning logging and monitoring is a big part of that answer.

Bad Code Example: A snippet of an ETL job with no logging:

# Bad: no logging, only prints (or not even that)
def run_etl():
    data = extract_data()
    transformed = transform_data(data)
    load_data(transformed)
    print("ETL job finished.")  # This is the only indication, not very informative.

run_etl()

If something goes wrong inside extract_data – say the source system is down – the script might just crash with a traceback, or worse, if there’s an except that passes (Mistake #3 style), even the print might still execute falsely. You have no record of what happened or what each step did. The print at the end doesn’t tell you how long it took, how many records were processed, or where any error occurred. And once the console is closed, that info is gone. There’s no persistence.

Good Code Example: Using the logging module and emitting meaningful messages and metrics:

import logging
logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(message)s")

def run_etl():
    logging.info("ETL job started.")
    data = extract_data()
    logging.info(f"Extracted {len(data)} records.")
    transformed = transform_data(data)
    logging.info(f"Transformed data, result has {len(transformed)} records.")
    load_data(transformed)
    logging.info("ETL job finished successfully.")

try:
    run_etl()
except Exception as e:
    logging.error(f"ETL job failed: {e}", exc_info=True)
    raise

Now we have an info log at each stage, including how many records were extracted/transformed (which is a basic metric to monitor). We wrap the run in a try/except that logs an error with stack trace if anything goes wrong. The logs are timestamped and have levels (INFO for normal progress, ERROR for failure) as configured in basicConfig. This could be further improved by logging to a file or external system, but even this console output is far better than nothing. It gives you a narrative of what happened leading up to any issue. In a framework like Airflow, logs are captured automatically, but you still need to log relevant info – not just have Airflow’s default. Good logging is critical for quick issue diagnosis. It’s often said that if it’s not logged, it didn’t happen – in data engineering, if you processed 5 million records and didn’t log that fact, when later the database has 4 million records, you’ll be scratching your head.

Beyond logging, monitoring extends to things like setting up alerts if a job doesn’t run or takes too long, tracking data quality metrics, etc. For example, if yesterday you processed 1 GB and today only 100 KB, maybe that’s a sign of an upstream problem – a good monitoring system would catch that anomaly.

How to fix/avoid it:

• Use the logging module (properly): As shown, configure logging at the start of your script. Use appropriate log levels (logging.info for routine progress, logging.warning for anything odd but not fatal, logging.error for exceptions). Don’t rely on print – prints won’t have timestamps or severity levels, and aren’t as flexible (plus, in some environments print output might not be captured). Logging can be directed to files, rotated, or sent to systems like CloudWatch or ELK stack later on – prints can’t. Logging is critical for diagnosing issues quickly.
• Log key events and data stats: At minimum, log the start and end of each major step. If you load data, log how much you loaded (rows, bytes, etc.). If you filter out records, maybe log how many were filtered. These become breadcrumbs to trace the pipeline’s behavior. But be cautious not to log sensitive info (like don’t log full passwords or large entire datasets). Summaries are enough.
• Persist logs: If running on a server or cloud, make sure logs are saved to a file or centralized system so you can review them after the fact. Most orchestrators do this for you (Airflow logs, etc.), but for custom scripts you might want to explicitly send logs to a file via logging.FileHandler. The key is that you shouldn’t lose the log when the process exits.
• Implement monitoring/alerting: This goes beyond code, but it’s worth mentioning. For important pipelines, set up alerts for failures (e.g., if a script exits non-zero or if an Airflow task fails, have it email/page you or the team). Also consider data quality checks – e.g., if the output row count is zero when it’s usually not, log a warning or trigger an alert. There are tools and frameworks (like Great Expectations for data quality, or custom scripts) that can run checks. The “No Logging/Monitoring” mistake is basically running blind – the fix is to assume things will fail eventually and be prepared. Logging is your preparation to debug, and monitoring is your safety net to catch issues proactively.

In summary, treat your pipelines as production systems that need the same level of observability as any software product. When you interview, being able to discuss how you added logging (“I use Python’s logging library to record each step, including counts of records processed, which helps in debugging”) or monitoring (“We use CloudWatch/Datadog to monitor job runtime and data metrics, and alert if things deviate”) will show that you think beyond just writing code – you think about running that code reliably. This is what separates a script kiddie from a true data engineer.

Mistake #10: Writing Code That Only Works on Your Laptop, Not in the Cloud

What it looks like: The code runs perfectly on your local machine, but when you deploy it to a cloud environment (or even a different OS or container), it breaks. This is often a culmination of several earlier mistakes and assumptions: hard-coded paths (assuming a certain directory structure or OS), missing environment setup (assuming certain packages or system libraries are present), not handling network or cloud-specific issues (like no retries for transient network calls), or using local resources (like writing to a local disk) when in a cloud run those might not be available or persistent. For example, you might write to /tmp/output.csv in an AWS Lambda function and expect it to stick around (it won’t beyond the ephemeral execution). Or you rely on a local GUI or specific locale/encoding that isn’t present in the minimal cloud runtime.

Why it’s a problem: “Works on my machine” is practically a meme in software – it highlights the disconnect between a dev environment and production. In data engineering, you have to assume your code will run in automated cloud jobs, Docker containers, or VMs that are not configured like your dev laptop. If your code isn’t portable, deployment becomes a fire-fight of patching things last minute. This slows down delivery and can cause outages when something you didn’t anticipate fails in prod. Employers want engineers who are aware of production environments. For instance, in an interview, they might ask “How would you deploy this pipeline to AWS?” – and if your answer doesn’t consider things like environment variables for config, packaging dependencies, adjusting file IO for cloud storage (S3, etc.), it shows a gap. Cloud-readiness is a must for modern data engineering; ignoring it limits you to toy projects.

Bad Scenario Example: Let’s illustrate with a scenario: You develop a Spark job on your laptop with local mode, it writes output to ./data/output.parquet. Everything seems fine. Then you try to run it on a Spark cluster in AWS. Suddenly, ./data/output.parquet is trying to write to the driver’s local disk, which might not be what you want (you might need HDFS or S3). Or maybe you used os.path.join with Windows backslashes in paths, and now the cluster is Linux and your paths are wrong. Another common one: using local filesystem for intermediate data in a cloud function. On your laptop, writing to /tmp and reading from it later works because the process is long-lived. In AWS Lambda or Google Cloud Functions, each invocation is stateless (or at least not guaranteed to run on the same container), so that approach fails. Also, perhaps your code calls an external API but you didn’t account for the cloud environment not having your local machine’s IP whitelisted, etc. These are all “works on my machine” issues.

Good Practices Example: To make code cloud-ready, you might: use environment variables for configuration (as discussed), use cloud storage paths (e.g., write to s3://bucket/output.parquet instead of local FS, or parameterize it), package your code and dependencies (maybe as a Docker image, or zip with requirements) so that the cloud environment has everything. A quick code illustration for portable paths and cloud usage:

# Good: Writing to cloud storage or parameterized path
import os
output_path = os.getenv("OUTPUT_PATH", "output.parquet")
df.to_parquet(output_path)

Now you can set OUTPUT_PATH to an S3 path in production, or a local path for testing. The code itself doesn’t hard-code the assumption. If deploying to AWS Glue or EMR, you’d configure that env var or job parameter accordingly.

Another example: ensure your code doesn’t rely on things like a specific Python version unless you control it, or at least document it. Containerizing the app can solve many “works on my machine” issues by encapsulating your environment (Docker ensures the environment is the same everywhere). If you’ve never containerized a data pipeline, it’s a great learning exercise – and something that often comes up in interviews for data engineering now (since many pipelines run on Kubernetes, etc.).

How to fix/avoid it:

• Test in an environment similar to production: Don’t wait until the last moment. If your target is a Linux server, test your code on Linux (even a VM or WSL on Windows, or a Docker container). If the target uses certain cloud services, try a small deployment or simulation. This will expose assumptions you may not realize you’re making.
• Use Cloud-Friendly Libraries/Patterns: For instance, if you know you’ll use AWS, consider using boto3 to directly interact with S3 rather than relying on local disk. Or use file abstractions like fsspec which can handle local or remote files uniformly. Also design stateless processing when needed (like in distributed or serverless contexts).
• Containerize or package your code: Containerization (with Docker) can capture your runtime environment fully – including OS, Python version, libs. “If it runs in Docker, it’ll run anywhere” (assuming the host can run Docker). If Docker is overkill, at least ensure you provide a requirements file and any setup scripts for the target environment, and use those on the cloud side. In some cases (like AWS Lambda), you bundle your code and libs into a deployment package – practice doing that to ensure you’ve included everything.
• Externalize configuration (again): We’ve harped on env vars and config files for credentials/paths already, but it bears repeating here. When moving to cloud, you often use different values (like different DB host, etc.), so make sure all those things are configurable. Cloud platforms have their ways to supply configs securely (secrets managers, parameter store, etc.) – use them instead of coding values in.
• Handle differences in platform: Simple things like path separators (os.path.join helps abstract Windows vs Linux paths), line endings (if you manually parse files, be aware of \r\n vs \n), or available disk. If you use multi-threading or multi-processing, consider that local vs cloud (in a managed service) might have different CPU counts or memory. It’s impossible to enumerate all such differences here, but the mindset is: be conservative in assumptions, and when in doubt, make it configurable or detectable. For example, if writing to a temp directory, use tempfile.gettempdir() rather than hardcoding /tmp, which will give an appropriate temp dir on any OS.

In essence, design for portability. The code should be as agnostic as possible about where it runs. If there are necessary differences, isolate them (like have a config flag “LOCAL_RUN” vs “CLOUD_RUN” that maybe toggles certain behaviors). By showing you understand this, you assure employers that your work won’t fall apart at deployment. It’s fine if you haven’t deployed large projects to cloud yet as a junior, but even in personal projects you can emulate good practices (like using Docker, or at least avoiding machine-specific quirks). This forward-thinking approach will save you a ton of pain and is highly valued in professional settings.

With these ten common mistakes and their fixes covered, you should feel more equipped to write Python code that is not only correct, but robust, efficient, and production-ready. It’s a lot to absorb, no doubt. But every big improvement in coding starts with identifying bad habits and consciously practicing better ones (remember that checklist!). As you incorporate these changes, you’ll notice your pipelines run smoother, your bugs become fewer, and your confidence as a developer grows.

Next, let’s solidify this knowledge by connecting it to real interview expectations and learning resources that can take you even further.

Watch: How to Bring Your Python Up to Interview Level

One of the best ways to internalize these patterns is to see them in action. We’ve put together a detailed YouTube video titled “ACE Your Data Engineer PYTHON Interview with CONFIDENCE” that walks through what hiring managers expect from your Python code and how to avoid pitfalls (just like the ones in this article). It’s essentially a guided breakdown of writing and speaking about Python at an interview-ready level.

In the video, our instructor goes through sample interview questions and live-codes solutions, pointing out common mistakes and better approaches. You’ll see how using functions, proper error handling, and efficient data structures can make a difference in an interviewer’s eyes. It also covers how to talk about your code – for example, explaining why you chose a dictionary over a list for a particular task (hint: O(1) lookups!). Sometimes, watching someone systematically write and refactor Python code can reinforce everything you’ve learned here. It helps you visualize the thought process of turning a naive solution into a polished one.

Make sure to give it a watch – especially if you learn better through visual and audio explanations. As you watch, pause and think: Would I have done it that way? If not, that’s a cue to adjust your own habits. The goal is to make writing clean, efficient Python your second nature. When you can do that, you won’t just pass interviews – you’ll shine in your daily work as well.

(Video link: ACE Your Data Engineer PYTHON Interview with CONFIDENCE – Data Engineer Academy on YouTube)

Get Interview-Ready with Our Python Data Engineer Interview Course

Reading about mistakes and best practices is a great start – now, imagine applying this knowledge in a structured, hands-on way, with expert guidance. That’s exactly what you’ll get in our Python Data Engineer Interview Course. This course is designed to bridge the gap between “I know basic Python” and “I can tackle real-world data engineering problems in Python under interview pressure.”

In the course, we delve deeply into the areas that commonly expose these mistakes, ensuring you learn to handle them with confidence:

• Data Structures & Algorithms for DE: Practice problems that involve large datasets, where you must choose the right data structures (lists vs. sets vs. dicts) and algorithms. You’ll get comfortable analyzing the performance of your code so you won’t write something that times out on big data.
• Hands-On Pandas and PySpark Challenges: Tackle exercises using pandas DataFrames and even PySpark RDD/DataFrame tasks. You’ll learn to avoid chained indexing issues, optimize joins and group-bys, and handle big data in a memory-efficient way.
• Debugging and Optimization: We intentionally include some buggy or slow code examples (the kind a junior might write) and then walk you through debugging and refactoring them. This builds your ability to spot issues (like the ones in this article) quickly and fix them systematically – a skill highly valued in interviews.
• Writing Clean, Modular Code: Every coding problem in the course is an opportunity to practice writing well-structured solutions. We emphasize function design, using clear variable names, adding comments or logging where appropriate – all the hallmarks of code that impresses in an interview.
• Mock Interview Scenarios: The course includes mock interview sessions (both live coding and take-home style) where you have to use Python to solve data engineering tasks. This is where you can demonstrate everything – from not hard-coding credentials when coding a pipeline task, to using try/except properly when ingesting data, to explaining your solution’s time complexity.

By the end of the course, you won’t just know what not to do – you’ll have a toolkit of what to do instead. You’ll be able to walk into a Python-based interview round and confidently write code that is efficient, clean, and correct, explaining your choices as you go. Many of our students even report that after the course, they started noticing their on-the-job code quality improved, leading to positive feedback from peers and seniors.

Ready to level up? Check out the Python Data Engineer Interview Course here and start your journey to becoming a standout candidate. The course is self-paced and comes with interactive exercises and solution walkthroughs. You can even start for free with some preview modules. It’s the fastest way to reinforce the lessons from this article with real practice – and to ensure that when you face those technical interviews, you’ll handle them like a pro.

Remember, investing in your skills is an investment in your career. Writing great Python code is a must for data engineering roles, and with the combination of this article, the YouTube video, and our course, you have a complete path to go from knowledge to mastery. We’re excited to see you transform from “junior” mistakes to senior-level performance. You’ve got this!

Frequently Asked Questions

Q: What level of Python do I need as a junior data engineer?
You should be comfortable with Python fundamentals (syntax, data types, loops, functions, etc.) but also with applying them in a robust way. It’s not enough to just know the syntax – you need to write code that can handle real data scenarios. That means understanding lists, dicts, sets deeply, knowing how to use libraries like pandas, and being aware of pitfalls like the ones in this article. You don’t need to be a software architect or know every advanced Python trick as a junior, but you do need to demonstrate clean coding habits. If you avoid the mistakes we’ve discussed (inefficient loops, poor error handling, etc.), you’re basically at the level you need to be. From there, you continue building more advanced skills, but a solid grasp of writing correct and clean Python is the baseline. Many junior data engineer interview questions revolve around these basics applied in practical ways – for example, manipulating data structures or writing a simple ETL script. So focus on writing code that’s not just working, but well-structured and clear.

Q: How can I quickly spot these mistakes in my existing code?
One approach is to use the checklist we provided earlier. Take one of your Python scripts or projects and literally scan it for each item: Are there any for loops that might be doing too much work? Any except: blocks without logging? Any hard-coded secrets or file paths? Also, run your code with warnings on (for pandas SettingWithCopyWarning, for example) to catch things like chained indexing – don’t ignore warnings. Peer code reviews are another great way: another person might spot a problem pattern faster because they’re not as attached to the code. Additionally, consider using linters or code analysis tools – some tools can catch things like mutable default arguments or too complex functions. Over time, as you learn to identify these issues, you’ll start noticing them while you write code. For now, a deliberate review process (perhaps after you finish a draft of a script) can work wonders. It might even help to keep a small handwritten (or digital) note of the top mistakes to avoid, and glance at it whenever you code, until it becomes second nature.

Q: Do I need advanced computer science algorithms, or is practical Python enough?
For most junior data engineering roles, practical Python is enough. You should understand fundamental CS concepts – like algorithmic complexity (knowing that O(n) is better than O(n^2), etc.) and maybe some basic algorithms (sorting, maybe simple graph or tree traversal if the job calls for it). But you’re typically not going to implement complex algorithms like you would in a pure software engineering interview for Google, for example. Data engineering interviews tend to focus on real-world problems: can you parse this file? Can you join these datasets? Can you aggregate logs by some criteria? That said, having some algorithmic knowledge can only help. It allows you to reason about performance and to choose the right approach (like knowing when a solution might be too slow). If you have a CS background, great – use it to write efficient code. If not, don’t panic: focus on the practical problems and learn what you need to solve them. Over time, working through problems in our course or on platforms like LeetCode can gently introduce you to the algorithmic thinking. But in summary, you don’t need to reinvent complex algorithms as a data engineer – you mostly need to effectively use Python and its libraries to manipulate data.

Q: How long does it take to fix these habits and feel confident in interviews?
It varies per individual, but generally a few weeks to a couple of months of focused practice can yield noticeable improvement. Since you likely already know how to code in Python, it’s more about changing habits and practicing better techniques. If you write Python daily at work, you could start integrating these improvements immediately and perhaps see changes in a matter of weeks. If you’re preparing full-time for interviews, you might spend a month deeply diving into Python coding challenges and refactoring your solutions until the good practices stick. The important thing is consistency – every piece of Python you write from now on, do it the “right” way consciously. Over a few projects or a few dozen practice problems, you’ll find that you don’t even think about the old way anymore. In our experience with learners, dedicating even 1-2 hours a day to practicing coding (and then reviewing your code for improvements) can make you interview-ready in about 4-6 weeks. Of course, it’s an ongoing journey – even after you land the job, you’ll keep learning and refining. But to reach a level of “I’m confident to handle interview problems,” a month or two of disciplined practice is often enough.

Q: What’s the best way to practice Python specifically for data pipelines?
One of the best ways is to actually build mini pipelines. For instance, pick a dataset (could be as simple as CSV of sales data), and write a script to simulate an ETL: extract from the CSV, do some transforms (aggregation, filtering, joining with another dataset), and load the result somewhere (maybe just to another file). As you do this, incorporate the best practices: use functions, add logging, handle errors, etc. You can also practice with real tools: try setting up an Airflow locally and write a DAG (Airflow uses Python) that executes some Python code you write – this will give you a feel for writing pipeline code in a production-like context. Another angle: contribute to open-source data engineering projects or do a project from our Academy modules. These will expose you to real scenarios, like reading from APIs, writing to databases, etc., using Python. Finally, use online coding challenge platforms with a data focus (like Kaggle for data processing tasks, or LeetCode’s database section which sometimes has to be solved with pandas). They provide problem prompts that simulate pipeline steps (e.g., “given transactions, produce a daily report”). In short: build things and solve problems that resemble what data engineers do. And always reflect – could my solution be more efficient or cleaner? That reflection part is where you solidify the learning.

Q: Can I become a data engineer if my background is in analytics or BI?
Absolutely! Many successful data engineers started in roles like data analyst or BI developer. In fact, having that background can be an advantage: you understand the end use of the data, reporting requirements, etc. To transition, you’ll want to focus on developing your software engineering skills (like Python programming, version control, etc.) and your knowledge of data engineering tools (like databases, SQL, ETL frameworks, cloud data services). If you’re coming from an analytics/BI background, you might already be good at SQL – that’s great, as it’s a core skill. Now you’ll augment it with Python and possibly learn about things like distributed computing (Spark), workflow schedulers (Airflow, etc.), and cloud platforms. Start by picking up Python for data engineering (this article is a good guide for that!). Work on some projects – perhaps building an automated pipeline that takes some data, transforms it, and loads it into a data warehouse where you, as an analyst, would then visualize it. By actually building the pipeline you used to just consume from, you’ll gain the engineering perspective. It might also help to get involved in your current company’s data engineering tasks if possible – even if just shadowing or assisting on a small project. Many analytical folks learn data engineering through on-the-job cross-functional projects. And of course, formal training like the courses we offer can fill in knowledge gaps systematically. In summary, yes you can become a data engineer – your domain knowledge from analytics is valuable, you just need to learn the engineering toolkit to go with it.

Q: How does the Python Data Engineer Interview course fit into my learning path?
Think of the course as a structured fast-track to everything we’ve discussed. If you’ve read this far, you already know what to avoid and some idea of how to do things better. The course will reinforce those concepts through hands-on practice and in-depth explanations. It fits into your learning path as the practice and polish step. Typically, one might:

1. Learn basic Python (sounds like you have this down).
2. Learn core data engineering concepts and tools (databases, SQL, maybe some cloud, etc.).
3. Focus specifically on applying Python in data engineering context.

It takes you through real scenarios and problems a data engineer encounters, under interview-like conditions, so you build both skill and confidence. If you’re preparing for interviews, you might use the course as a central part of your study plan for a few weeks. It will point out common pitfalls (many of which we covered here) and make sure you get to practice avoiding them. If you’re not in interview mode yet but just want to get better, the course will still accelerate your growth – it’s like distilling years of on-the-job Python experience into a series of lessons and exercises. We often see learners take the course, then apply those skills immediately at work and impress their teams.

In short, the course is a way to ensure you’ve got the skills that this article talks about. It’s one thing to read about fixes; it’s another to implement them under various circumstances. The course provides that implementation practice in a guided way. Plus, it’s structured by experts who know what companies ask, so you won’t miss out on important topics. Think of it as the difference between reading about how to ride a bike vs actually riding one with a coach by your side – the latter is more effective and builds real confidence.

No matter where you are on your learning path – whether transitioning from another field or fresh out of school – the Python Data Engineer Interview course can adapt to you. It starts from fundamentals and goes to advanced topics, so you can find value at each step. We designed it to be comprehensive but digestible, much like this article aims to be. We’re here to support you every step of the way on your journey to becoming a skilled data engineer!

Chris Garzon

Christopher Garzon has worked as a data engineer for Amazon, Lyft, and an asset management start up where he was responsible for building the entire Data Infrastructure from scratch. He is the author “Ace the Data Engineer Interview” and has helped 100’s of students break into the data engineer industry. He is also an angel investor, an advisor to multiple to multiple start ups, and the founder and CEO of Data Engineer Academy.