Data Engineer Portfolio Review Checklist (2026): What Hiring Managers Actually Score

A hiring manager opens your data engineering portfolio on GitHub at 10 PM, sifting through dozens of applicants. You have mere minutes to impress. Will your projects make them nod in approval or sigh in disappointment? In 2026’s competitive data job market, your portfolio can make or break your first job opportunity. It’s often the secret sauce that differentiates two resumes with similar experience. But not just any collection of code will do – it needs to hit the points that hiring managers actually care about.

For many entry-level data engineers, building a portfolio is daunting. Maybe you’ve completed a few online projects or bootcamp assignments and wonder if they’re “good enough.” The truth is, most hiring managers use an unspoken checklist when evaluating your portfolio. If your work looks like a toy exercise or is poorly presented, they’ll move on quickly. However, a well-crafted portfolio that demonstrates real problem-solving with modern data tools can instantly catapult you into that top 10% of candidates who get remembered (and interviewed).

Let’s pull back the curtain on what hiring managers in 2026 really look for in a data engineering portfolio – and how you can check all the right boxes to land your dream job.

Quick summary: A 2026-ready data engineering portfolio showcases 2–3 real-world projects using modern tools (think Airflow, Spark, Snowflake, etc.), with clear documentation and an emphasis on solving actual business problems. Quality beats quantity every time.

Key takeaway: Hiring managers care less about which fancy technologies you list and more about how you applied them. One deep, well-documented project that mimics a real data pipeline is worth more than five shallow demos.

Quick promise: Follow this checklist, and you’ll build a portfolio that doesn’t just pass a hiring manager’s review – it wows them. By the end, you’ll know exactly how to make your portfolio a conversation starter and an interview magnet. Learn how to code and land your dream data engineer role in as little as 3 months (with the right guidance, you can fast-track these skills and projects).

Why Your Data Engineering Portfolio Matters in 2026

In 2026, data engineering is more critical than ever. Every company is dealing with huge volumes of data, real-time analytics demands, and AI initiatives that all depend on reliable data pipelines. Listing skills on a resume isn’t enough for an entry-level candidate – hiring managers want proof. A strong portfolio shows you can actually build the systems that move and transform data in the real world. It bridges the gap between “I took a course on Spark” and “I built a Spark pipeline that solves X problem.”

Importantly, having a portfolio immediately sets you apart. According to industry observations, fewer than 1 in 10 junior candidates include a portfolio with their application. So when you do, you’re instantly more memorable. It signals proactiveness and passion. Moreover, your portfolio projects give you concrete talking points in interviews – instead of hypotheticals, you’ll discuss how you designed a data warehouse schema or debugged a broken pipeline. In short, a well-crafted portfolio can be the deciding factor for a hiring manager choosing who gets the offer.

(Pssst – Not sure where to begin? Don’t worry. Even if you’re new to these tools, you can learn them step-by-step. Learn how to code and land your dream data engineer role in as little as 3 months through our personalized training, and build portfolio projects that impress.)

What Hiring Managers Actually Look For (The 2026 Portfolio Checklist)

When a hiring manager reviews your data engineering portfolio, they’re mentally scoring it across a few key areas. Think of it as a checklist that separates the strong candidates from the average. Below are the criteria hiring managers in 2026 are actually looking for in an entry-level data engineer’s portfolio:

1. Solving a Real Problem (Relevance Over Toy Projects)

Checklist: Does each project address a meaningful problem or use case?

Recruiters and hiring managers are not impressed by generic “textbook” projects. If your portfolio is full of common tutorial examples (the classic Titanic dataset or a trivial ETL of a CSV), it won’t stand out. Instead, frame your projects around real-world scenarios. For example, you might simulate an e-commerce company’s need for a real-time inventory restock alert system, or build a pipeline to aggregate and clean city transit data for analysis. The key is to show that you understand why the project matters. Hiring managers love to see a short description of the problem or business question up front: what you did and why it’s important. This demonstrates business context and critical thinking – you’re not just coding for the sake of coding, you’re solving a problem.

Pro tip: Include a brief problem statement at the top of each project’s README. For instance: “Goal: Build a pipeline to identify and notify when products are low in stock across 100+ retail stores, to prevent out-of-stock scenarios.” This instantly tells the reviewer you’re thinking like a professional, focusing on impact.

2. Realistic Data & Complexity (No Easy Way Out)

Checklist: Did you use real, messy, or large-scale data that mimics real job challenges?

Hiring managers can quickly tell the difference between a contrived class project and a realistic one. Real-world data is messy, large, and often unstructured – and showing you can handle that is a big plus. If all your projects use small, clean datasets (e.g., a tidy CSV from Kaggle that’s been used a million times), it doesn’t prove you can deal with challenges like missing values, JSON logs, or streaming data. In 2026, companies care about scalability and variety of data. So, try to incorporate datasets that are sizable or come from real sources: maybe millions of rows, or data fetched from an API, or a stream of events. Even if you generate synthetic data, mention the volume and how you introduced irregularities to simulate reality.

Also, demonstrate complexity in the pipeline itself. Did you just write one Python script and call it a day? Or did you create a multi-step pipeline with dependencies? A complex project might involve multiple stages (ingestion, processing, storage, analysis) or handling different data sources. This doesn’t mean you need to complicate things unnecessarily – but it should reflect real engineering work. For example, instead of loading one file into a database, a more impressive project could pull data from an API daily, append it to a data lake, transform it with Spark, and then load aggregate results into a warehouse. Show that you’re comfortable with the scale and complexity typical of enterprise data.

3. Modern Tools & Technologies (Industry-Standard Stack)

Checklist: Are you using the tools and platforms that data teams use in 2026?

Tooling is a huge signal. Hiring managers scan your project for the technologies you used, and they’re specifically hoping to see modern, widely-used data engineering tools. The exact tools can vary by company, but some staples in 2026 include:

• Apache Airflow (or similar orchestrators like Prefect): For scheduling and managing workflows. Including an Airflow DAG in your project that automates tasks immediately says you know how to productionalize pipelines.
• dbt (Data Build Tool): For SQL-centric data transformations and modeling. If you showcase a project where you used dbt to build data models in a warehouse, you earn extra points for following best practices in ELT.
• Apache Spark (or cloud equivalents like Databricks): For big data processing. Even a small Spark job that processes data in parallel demonstrates you can handle large-scale data processing if needed.
• Cloud Data Warehouses like Snowflake or Google BigQuery (or AWS Redshift): Modern data engineering portfolios often interact with at least one cloud service. For instance, loading your cleaned data into Snowflake and running a few queries shows you’re comfortable with cloud storage and analytics.
• Version Control with GitHub: This one is non-negotiable. All your code should live on a GitHub (or GitLab/Bitbucket) repo, neatly organized. Employers expect you to know basic Git. A bonus is if you have meaningful commit history, issues, or even CI/CD setup for your project.
• Streamlit or Dashboarding Tools: While not a core requirement, having a simple Streamlit app, a Jupyter Notebook with visualizations, or a Tableau/PowerBI dashboard to present results can set you apart. It shows you can communicate data findings and build simple interfaces – a nice touch for end-to-end completeness.

You don’t need to use all of these in one project (in fact, please don’t cram tools just to name-drop them). But across your portfolio, try to cover a few of these key tools. If you built one project with just Python and Pandas, consider leveling it up by introducing Airflow to schedule it, or moving the data to BigQuery for analysis with SQL. The goal is to show you’re up-to-date with the data engineering ecosystem. A hiring manager seeing familiar tools in your repo will immediately think, “Great, they’ve worked with the kind of stack we use. They’ll ramp up faster on our team.”

4. Code Quality and Best Practices

Checklist: Is your code clean, organized, and following best practices like a production codebase?

Remember, how you write code matters just as much as what your code does. A hiring manager or senior engineer reviewing your repository will notice things like structure, readability, and professionalism in your code. A few pointers to get this right:

• Clean, Modular Code: Break up scripts into logical modules or functions. Avoid one giant 500-line script. Use clear function/variable names. If appropriate, structure your repository with folders (e.g., a src/ directory for code, a data/ folder, etc.). This shows you understand maintainability.
• Requirements and Environment: Include a requirements.txt or environment.yml for Python projects so others can install dependencies. If you used Spark or Hadoop, mention the version. Using Docker? Provide a Dockerfile. These details prove you know how to set up reproducible environments – a big part of real-world engineering.
• Follow Standards: For Python, adhering to PEP8 style (indenting, line length, etc.) and using docstrings/comments where needed makes your code look professional. Similarly, SQL queries should be formatted nicely. It’s not about perfection, but showing you care about code quality.
• Error Handling & Logging: If your project includes robust error handling or logging (e.g., printing meaningful log messages or writing logs to a file/service), it demonstrates a production mindset. Tools like Python’s logging module or using Airflow’s logging come in handy.
• Performance Considerations: While you might not be handling truly big data as a beginner, mention any steps you took to make your code efficient (vectorized pandas operations, using Spark for parallelism, proper indexing in SQL, etc.). It shows you think about optimization, not just brute-force solutions.

When a hiring manager opens your code, they’re quietly asking, “Would I trust this person to write code for our production pipeline?” By writing clean and structured code, you’re effectively saying “Yes – look, I code like an engineer, not just a student.” Even if they don’t run your code, this quality will come through in a quick skim.

5. Documentation and Clarity (Tell the Story)

Checklist: Do your projects include clear explanations, documentation, and results that anyone can understand?

This is huge and often overlooked. Great engineers communicate what they did. For your portfolio, the documentation primarily means your README file and in-line comments or notebooks. Here’s what to do:

• Project README: Every project should have a README.md that serves as a guide. At minimum, it should have:
  • A summary of the project (the problem statement and solution approach).
  • Technologies/Tools used (a list of key tools and languages).
  • Instructions on how to run the project (install dependencies, how to execute the pipeline or code, sample command-line usage, etc.).
  • If possible, an architecture diagram or workflow graphic. A simple flowchart showing data sources, steps in the pipeline, and outputs can convey the scope at a glance.
  • Results/Insights: Did your pipeline produce a cool dashboard or reduce processing time by 80%? Mention what the outcome was. For example, “In the end, this pipeline processes 1 million records in under 2 minutes and provides a live dashboard of analytics in Streamlit.”
• Comment and Explain: In code or notebooks, comment key sections, especially if some tricky logic is going on. You might add a short explanation of your strategy (“# using a window function here to handle late-arriving data”).
• Visuals and Examples: Including a screenshot of your Streamlit app or a snippet of output can make your project feel more tangible. If your data pipeline populates a dashboard, include an image of it in the README. If you have charts from analysis, show one.
• Clarity for Non-Experts: Assume the person reading is technical but not deeply familiar with your specific project. Avoid overly academic language; keep it straightforward. Think of your documentation as a quick tour: if a busy manager spends 60 seconds on your repo, the README should tell them the what, why, and how of your project in plain language.

A portfolio that’s well-documented screams professionalism. In fact, many hiring managers will look at the README before they even peek at the code. If the README is missing or sparse, some might not bother digging further. Show that you respect the reader’s time by giving a clear roadmap of your project. It’s a lot like writing a story: set the context, describe the challenge, show how you solved it, and highlight the happy ending (results).

6. Depth and Completeness of the Project

Checklist: Did you take the project far enough to demonstrate end-to-end understanding?

Depth is what separates a great portfolio project from an average one. It’s about going beyond the bare minimum. Hiring managers are looking for signs that you understand the full lifecycle of data engineering, even if only conceptually. Here are ways to add depth to your projects:

• End-to-End Pipeline: Whenever possible, implement the project from data source all the way to final output. For example, don’t stop at transforming the data – also load it into a database and perhaps query it or visualize it. Or if you built a data lake, try adding a layer that uses that data (like a simple analysis or ML model consuming the cleaned data). This shows you appreciate how pipelines feed into real use cases.
• Automation & Scheduling: If you triggered everything manually, it’s not truly reflective of production. Use a scheduling mechanism (Airflow DAG, Cron job, or even a simple loop in code that simulates daily runs) to show the pipeline can run repeatedly. Mention how frequently it’s meant to run (hourly, daily, in real-time streaming).
• Data Quality & Error Handling: Include some data validation or quality checks. For example, you can use assertions or a tool like Great Expectations to ensure no null values in a critical field, or that yesterday’s data falls within expected ranges. Also, consider how your pipeline handles failures – do you retry? Do you alert? Even a note in the README “If a step fails, Airflow will retry it 3 times and send an email alert” demonstrates a production-oriented mindset.
• Scalability Thoughts: Add a note on how your solution could scale. “Currently processes 100k records in 5 minutes on my laptop; could be scaled to millions by deploying on Spark or using cloud resources.” Even if you didn’t implement the big data version, acknowledging it shows insight.
• Optional Enhancements: Discuss what you’d do next if you had more time. For instance, “Next steps: deploy this pipeline on AWS with Terraform for infrastructure, integrate a monitoring tool like Prometheus to track pipeline performance.” You don’t have to actually do all of that, but showing you think beyond the project as-is can earn you points for vision.

In essence, you want to convince the hiring manager that you didn’t just follow a tutorial and stop when it worked one time. You treated the project like a production system: it runs reliably, handles bad data, and could be maintained or scaled. Depth over breadth is important – it’s better to fully flesh out one complex project than to have five shallow ones. When they find at least one example in your portfolio that’s polished and complete, they’ll feel confident you can handle real projects on the job.

Strong vs. Average Portfolio Projects: Examples

It can be hard to judge your own projects objectively. To help, let’s compare what a “strong” portfolio project looks like versus an “average” one in an entry-level data engineering portfolio:

• Strong Project Example: “Real-Time Streaming Analytics for Retail.” Imagine a project where you simulate retail sales data streaming from point-of-sale systems. You set up Apache Kafka to ingest sales events continuously. An Apache Spark Structured Streaming job processes these events in near real-time to calculate metrics (e.g. current inventory levels, trending products). The processed data lands in a Snowflake warehouse, where you’ve defined tables with dbt for daily aggregates. Finally, you built a small Streamlit dashboard that store managers could use to see live sales and inventory alerts. The project includes an Airflow DAG to kick off batch processes (like daily reconciliation jobs) and uses logging/monitoring to track pipeline health. Everything is documented: there’s an architecture diagram, and the README explains how real-time data helps businesses react faster (e.g., preventing out-of-stock by alerting when inventory is low).
  Why it stands out: This project shows breadth and depth: streaming + batch, multiple tools working together, a clear business use case (retail analytics), and polished presentation. A hiring manager sees you’ve touched on challenging concepts (stream processing, integration of systems, dashboarding results) and tied it to business value.
• Average Project Example: “ETL of a Public CSV to SQL.” This typical project might involve taking a well-known open dataset (say, the Titanic passengers or a Kaggle dataset), writing a simple Python script or Pandas code to clean it, and then loading it into a local SQLite or CSV file. Perhaps there’s a Jupyter Notebook showing some charts or a simple conclusion like “X number of passengers survived.”
  Why it’s not enough: While there’s nothing wrong with this as a learning exercise, it doesn’t differentiate you. The data is small and clean (doesn’t show you can handle real pipelines), the tools are minimal (Python and Pandas only, no orchestration, no cloud or big data tools), and there’s often little context or complexity. A hiring manager has likely seen dozens of similar projects. It doesn’t tell them whether you could build a complex pipeline at their company. If this is currently what you have – think about how you can level it up. For instance, could you turn it into an end-to-end project by running it on a schedule and deploying the database to the cloud? Or perhaps incorporate a more interesting data source to solve an actual question (e.g., integrate Titanic data with weather data to see if weather affected survival, just as a twist). Always aim to elevate a basic project into something that demonstrates more skills and insight.

In short, strong projects tackle realistic scenarios, use multiple relevant tools, and are presented as if they were production solutions, whereas average projects often look like class assignments with limited scope. Review your own portfolio through this lens – would a hiring manager see the projects and think, “This person can handle real tasks,” or “This looks like homework”?

How to Structure and Present Your Portfolio for Impact

Even great projects can fall flat if they aren’t presented well. Think of portfolio presentation as the packaging for your awesome content – it needs to be appealing and easy to navigate. Here are tips on structuring your portfolio to maximize clarity:

• Use GitHub Effectively: For most entry-level data engineers, GitHub is the go-to platform for your portfolio. Ensure your GitHub profile is tidy. Consider creating a pinned repository (or a dedicated portfolio repository) that serves as an index, linking to each of your project repos with a short description. This way, a recruiter landing on your GitHub sees a “Portfolio” repo or section and can quickly find your best work.
• One Project, One Repository: It’s usually best to give each major project its own repository. Name the repo descriptively (e.g., real-time-retail-analytics-pipeline rather than Project_2). This keeps project contexts separate and avoids confusion. Each repo should contain all code, data samples, and documentation for that single project.
• Organize Files and Folders: Within a project repo, follow standard conventions. For example:
  • Have a README.md at the root explaining the project (as discussed earlier, include setup and usage instructions).
  • If applicable, a data/ folder for sample datasets or a script to download data.
  • A src/ or pipeline/ directory for your source code (Python scripts, SQL queries, dbt models in a models/ folder, etc.).
  • Perhaps a notebooks/ folder if you used Jupyter for exploration or demonstration.
  • docker/ or infrastructure/ if you have Dockerfiles or Terraform scripts for cloud setup.
  • Tests or expectations could live in a tests/ folder.
    This level of organization shows you know how to structure a codebase – a subtle but powerful signal of competence.
• Hosting and Accessibility: If possible, host your project outputs in a way that’s easy to see. For example, if you built a small web app or dashboard, consider deploying it (even temporarily) and sharing a link. If not, screenshots or even a short demo GIF in the README can help. For pipelines, you might not have a running service to show, but you can at least provide example outputs or mention “you can run the script and it will do X – here’s a sample output file or log.”
• Clarity and Conciseness: Busy reviewers won’t read a novel. So while you want thorough documentation, also practice brevity and highlighting key points. Use bullet points or tables in your README to call out important aspects (e.g., “Tech stack: Python, Airflow, Spark, AWS S3, Snowflake”). Use headings and bold text in your documentation to make it skimmable. The same goes for any write-up or blog post if you have one accompanying the project.
• Portfolio Website (Optional): Some candidates create a personal website to showcase their portfolio (with a nice UI, project pages, etc.). This can look very professional, but it’s not strictly necessary for data engineering roles. Hiring managers primarily care about the content (projects) over the container. If web design isn’t your forte, a well-structured GitHub and a good LinkedIn post about your project can do the trick. However, if you enjoy it, a simple site or even a Notion page where you break down your projects in a narrative form can act as a portfolio front page. Just ensure you link directly to your code repositories too – because technical folks will want to dive into the code after reading.
• Consistency: Ensure your tone and style is consistent across projects. If one README is extremely detailed and another has nothing, it looks odd. Aim for each project to have a similar level of completeness. It’s better to thoroughly polish 2 projects than to have 5 where only one looks great and the rest are half-baked.

Finally, make it easy for someone to find and review your portfolio. Include the link to your GitHub or portfolio site on your resume, in your LinkedIn, and even in your email signature if you want. The easier you make a hiring manager’s job, the more likely they are to actually look at your work. When they do, a clear and structured presentation ensures they come away with a positive impression – understanding your skills without any frustration in navigating.

Tips for Creating a Standout Data Engineering Portfolio

To wrap up the main section, here’s a quick list of practical tips to help your portfolio shine among the rest:

• Focus on Quality, Not Quantity: It’s worth repeating – 2-3 excellent projects beat 10 mediocre ones. Don’t stretch yourself trying to cover every topic. Instead, pick a couple of areas and do them really well.
• Emulate Real Work Scenarios: Ask yourself, “Is this something a data engineer might actually do on the job?” A project designing a data warehouse for a sales company or setting up a data lake for analytics sounds like real work. In contrast, “my Kaggle competition entry” might not translate directly to business value. Shape your projects to resemble mini work projects.
• Incorporate Both Batch and Real-Time (if you can): Show versatility by including one project that’s a batch ETL/ELT pipeline (daily or hourly jobs processing data in bulk) and another that handles real-time or streaming data. This covers both worlds and signals you understand when to use each approach.
• Use Cloud Services (even free tier): Cloud knowledge is pretty much expected now. You can use free tiers or local simulate tools. Deploy a small database on AWS or use GCP’s free BigQuery queries. Storing a dataset in an S3 bucket and accessing it, for example, shows you can work beyond your local machine. Mention any cloud services you touch.
• Showcase Data Modeling and SQL Skills: If any project involves a database or warehouse, highlight the schema you designed. For instance, include an ERD (Entity Relationship Diagram) or describe your dimensional modeling (star schema) if you built one. Hiring managers love to see that you understand how to model data for analytics – it’s a core skill that’s sometimes hard to glean from just code.
• Keep Learning and Updating: The data landscape evolves quickly. If you built a portfolio project last year, review it: is there a new tool or a better practice you’ve learned since that you can incorporate? Updating a project (and documenting the update in a CHANGELOG or in the README “Update 2026: migrated pipeline to use Delta Lake for reliability”) can show you are continuous in learning. It’s also perfectly fine to replace old projects with new ones as your skills grow.
• Prepare to Discuss in Interviews: A great portfolio will almost guarantee you’ll be asked about it. Be ready to talk through any project in depth. Know your design decisions, be honest about any trade-offs or things you’d improve, and emphasize what you learned. This enthusiasm and clarity in explanation can often be the thing that convinces the team to give you an offer. After all, the portfolio’s purpose is not just to get an interview, but to help you ace it.
• Passion and Personality: Let your interests shine through your projects. If you love sports, do a data engineering project on streaming sports stats. If you care about climate data, build a pipeline around that. When you’re genuinely interested in the subject, you’ll naturally put in more effort and be more excited discussing it. Hiring managers often remember candidates who had a unique, passion-fueled project because it stands out from the cookie-cutter ones.

With these tips in mind, you’re well on your way to creating a standout portfolio that truly represents you as an aspiring data engineer. It’s a bit of work to assemble all this, but remember: your portfolio is an investment in your career. It’s a product you deliver that markets you. Make it count, and you’ll see the payoff when you start landing interviews and job offers. Good luck, and happy building!

Quick Facts — Data Engineering Portfolio

• Portfolios are rare: Only about 10% of entry-level data engineering candidates submit a portfolio, so a strong one instantly puts you in an elite group.
• Hiring manager skim time: On average, a hiring manager might spend 5–10 minutes looking at your portfolio initially – a clear README and well-organized projects are crucial to hook their interest fast.
• Top skills to showcase: Common job postings in 2026 emphasize SQL, Python, cloud platforms (AWS/GCP), Apache Airflow, Apache Spark, and dbt – integrate one or more of these into your projects to align with industry demand.
• Optimal project count: Aim for 2–4 portfolio projects that cover different aspects of data engineering (e.g., one streaming pipeline, one warehouse ETL, one data lake or big data project) to demonstrate range without overwhelming reviewers.
• Update frequency: Treat your portfolio as a living document – keep it updated. Adding improvements or new projects every few months shows continued learning and commitment to staying current.

Frequently Asked Questions (FAQ)

How many projects should my data engineering portfolio have?
Focus on quality over quantity. For an entry-level data engineer, having 2 to 3 solid projects is typically enough. Each project should be substantial and demonstrate different skills or tools. It’s better to showcase a few well-executed projects than a dozen half-finished or trivial ones. If you have more than three projects, consider highlighting the best ones and listing others as supplemental.

Is a GitHub profile enough, or do I need a personal website for my portfolio?
A well-organized GitHub profile is usually enough for data engineering roles. Hiring managers and engineers are very used to checking GitHub repositories. Just make sure your GitHub is clean: use the pinned projects feature to showcase your portfolio projects prominently. A personal website can be a nice touch – it can offer a more visual or narrative presentation – but it’s optional. If web development isn’t your strength, don’t worry; a great GitHub repo with clear documentation will do the job. You can also share your GitHub project links on LinkedIn posts or your resume to direct people there.

What if I haven’t used certain tools like Airflow or Spark yet? Should I still include those?
If you haven’t used a tool, don’t list it as if you have – honesty is important. However, you can plan a project that helps you learn that tool. For instance, if you’ve never used Airflow, start by converting one of your existing script-based projects into an Airflow DAG. It’s fine to begin with the basics. Hiring managers don’t expect you to be an expert in everything, but they do value initiative. So, rather than faking knowledge, invest time in a small new project to get hands-on experience with that tool, then include it. Everyone starts somewhere – demonstrating that you’re picking up new technologies (and documenting that journey) can actually impress employers more than just listing buzzwords.

How can I come up with strong project ideas for my portfolio?
Think about real problems or interests you have, and then add a data engineering spin to them. Some idea sources:

• Look at common scenarios in businesses (e.g., building a data warehouse for sales data, creating a data pipeline for user logs, streaming analytics for social media feeds).
• Tap into your personal interests (if you like sports, make a pipeline for sports statistics; if you’re into finance, ingest stock market or cryptocurrency data for analysis).
• Explore public datasets and open APIs. For example, city open data portals have data on everything from transit to climate – pick something and imagine a company or use-case around it.
• Check out online communities (Reddit’s r/dataengineering or blogs) where people share project ideas. You’ll often find suggestions like “IoT sensor pipeline using Kafka” or “ETL pipeline of NASA data.”
• Lastly, consider re-building a scaled-down version of a data architecture you read about. If a case study says “Company X built a recommendation engine pipeline using Spark and Kafka,” try to emulate that with a smaller data sample.

The best project ideas are those that excite you (so you’ll stay motivated to finish them) and that reflect tasks a data engineer might actually do. And remember, you can always start simple and then iterate to add more complexity or features.

How do I share my portfolio with hiring managers or make sure they see it?
There are a few ways to put your portfolio in front of hiring managers:

• Resume & Cover Letter: Always include a link to your GitHub or portfolio site on your resume (near your contact info). You can even briefly mention a standout project in your cover letter or email (e.g., “Attached is my resume, and you can find my data engineering projects (like a real-time data pipeline) on my GitHub profile here: [GitHub link].”).
• LinkedIn: Upload a post or feature your projects on your LinkedIn profile. For instance, write a short post about what you built and learned, with a link to the repo. Recruiters often scan LinkedIn; showing off a project there can catch their eye.
• Networking: If you’re talking to recruiters or engineers (at a career fair, meetup, or even online in forums), mention your portfolio projects. It’s a great conversation starter: “I recently built a data pipeline that does X… I’d love feedback if you have a chance to look at it.”
• Apply it in Interviews: If you get an initial phone screen, you can reference your portfolio: “One of my projects was building a data lake on AWS; I’d be happy to talk about what I did there.” This prompts interviewers to take a look either during or before the next round.
• Be Proactive: For roles you’re keen on, you might even tailor a project to that company’s domain and mention it. For example, “I saw your company works with streaming data, I actually built a small streaming project on my GitHub.” This isn’t necessary for every application, but it can be a differentiator for ones you really care about.

In essence, don’t be shy about your portfolio – you worked hard on it, so make sure people know it exists! Most hiring managers will appreciate the initiative, and it often becomes a strong talking point that can lead to a hiring decision in your favor.

Key Terms Glossary

• Apache Airflow: An open-source platform for orchestrating and scheduling data workflows via directed acyclic graphs (DAGs), widely used to automate ETL pipelines.
• dbt (Data Build Tool): A framework for managing data transformations in SQL, allowing engineers to build modular SQL pipelines with version control, testing, and documentation (popular for ELT in data warehouses).
• Apache Spark: A distributed computing engine for big data processing. Spark enables fast parallel processing of large datasets and is used for tasks like ETL, batch processing, streaming analytics, and machine learning at scale.
• Snowflake: A cloud-based data warehouse known for its scalability and performance. Snowflake separates storage and compute, handles structured and semi-structured data, and is often used to store and query large volumes of data with ease.
• BigQuery: Google Cloud’s serverless data warehouse solution that can query massive datasets using SQL. BigQuery is highly scalable and optimized for big data analytics without needing to manage infrastructure.
• Streamlit: An open-source Python library for quickly creating web apps to showcase data science or data engineering projects. Streamlit apps are often used to build simple dashboards or interactive demos for portfolio projects.
• GitHub: A web-based platform for version control and collaboration, using Git. In the context of portfolios, GitHub is where you host your code repositories so that hiring managers can review your projects, track your commits, and see documentation.
• ETL/ELT: Stands for Extract, Transform, Load / Extract, Load, Transform. It’s the process of moving data from sources to a destination (like a data warehouse). In ETL, data is transformed before loading into the target system. In ELT, data is loaded first (often into a warehouse) and then transformed using the warehouse’s power (as is common with tools like dbt).
• Data Pipeline: A series of data processing steps that moves data from raw source to a final destination (and possibly through intermediate stages). A pipeline may include extraction, cleaning, transformation, and loading, and can be batch or real-time. It’s the backbone of data engineering work, ensuring data flows smoothly for analytics or applications.

Now you’re equipped with a comprehensive checklist and understanding of what makes a stellar data engineering portfolio in 2026. It’s time to apply this knowledge: refine your projects, update that documentation, and put yourself out there. With a portfolio that hits these marks, you’ll show hiring managers you’re not just another applicant – you’re the data engineer they’ve been looking for. Good luck on your journey, and happy coding!

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.