Portfolio to Paycheck: 7 Data Engineering Projects Hiring Managers Actually Want in 2025

How do you turn a data engineering portfolio into an actual paycheck? In the competitive 2025 hiring landscape, the answer is simple: show, don’t tell. Hiring managers are swamped with resumes listing Python, SQL, and cloud experience. What really makes you stand out is proof that you can use those skills to build real, working data systems. In other words, your project portfolio is your interview currency. It bridges the gap between “I’ve taken courses” and “I can do this job for real.”

If you’ve followed our latest article, The Fastest Way to Learn Data Engineering in 2025, you know that a hands-on approach and a “winner” e-portfolio of projects is key to landing a job fast. But which projects will actually impress employers? To find out, we tapped into current hiring trends and gathered insights from our Data Engineer Academy coaches and alumni. They’ve been on both sides of the hiring table, and their own success proves it – the jobs after Data Engineering Academy training speak for themselves. Many graduates and coaches have secured roles at top companies, including Dell, Google, Amazon, Facebook, Lyft, FedEx, and The Walt Disney Company. They all agree on one thing: practical data engineering projects that mirror real-world scenarios will make you a strong candidate.

In this article, we’ll cover:

• Building a strong data engineering portfolio in 2025 is essential to stand out from candidates who only list technical skills.
• Employers want to see proof of your ability to design, build, and manage real-world data systems, not just theory.
• The most effective projects include data pipelines, cloud-based warehouses, ingestion and transformation workflows, and AI/ML integration.
• Practical, production-ready projects show hiring managers that you can handle real challenges at scale.
• A portfolio hosted on GitHub with clear documentation, architecture diagrams, and outcomes adds credibility and impact.
• Starting with foundational ETL/ELT pipelines and progressing to advanced, AI-driven solutions creates a natural growth path.
• A well-curated portfolio acts as a master key to unlocking data engineering opportunities in 2025.

By the end of this article, you’ll know exactly what to build to strengthen your portfolio and move closer to that job offer. Think of it as a roadmap for how to become a data engineer with real, marketable skills.

New to Data Engineering? Start with our fast-track guide:

Essential Data Engineering Projects for Your Portfolio

Building a strong portfolio matters when you’re pursuing a data engineering role. The right projects can demonstrate your technical abilities and problem-solving approach to potential employers.

Make your portfolio into evidence. Watch the tutorial video below to learn what to construct, which stack to utilize, and how to prepare projects for a portfolio.

The following project kinds can help you make a stronger case:

• Data Pipeline Project
  Build a complex pipeline using real-time data processing to show you can handle streaming data effectively. This type of project demonstrates your understanding of data flow architecture and your ability to work with live data sources.

• Data Warehouse Implementation
  Create a warehousing solution that pulls together information from multiple sources. This showcases your integration skills and your ability to design systems that support business intelligence needs.

• Data Ingestion and Transformation
  Develop a project focused on ingesting, cleaning, and transforming raw data to prepare it for analysis. This highlights your expertise in the foundational aspects of data preparation.

• Data Visualization
  Create visualizations with your processed data to highlight your presentation capabilities and analytical insights. Strong visual communication skills set data engineers apart in collaborative environments.

• Big Data Project
  Use a big data stack to manage and analyze large datasets. This demonstrates your proficiency with distributed systems and your ability to work at scale.

• Open-source Projects
  Contribute to existing open-source tools or build your own projects focused on data engineering frameworks. This shows your engagement with the broader community and your commitment to continuous learning.

Each project should tell a story about your technical growth and problem-solving abilities. Choose projects that align with the types of roles you’re targeting and the technologies commonly used in your desired industry.

Project Ideas for Beginners

If you are just starting your journey as a data engineer, consider these beginner-friendly data engineering projects:

• Data Extraction. Build a simple project that extracts data from a public API, such as weather data, and stores it in a data warehouse.
• Data Analysis. Analyze sales data to identify trends and patterns, demonstrating your ability to perform data analysis.
• Python Data Projects. Use Python to create scripts that automate data processing tasks, enhancing your engineering skills.

Creating a Strong Project Portfolio on GitHub

To show prospective employers your work, host your data engineering portfolio on GitHub. Be careful to add:

• Thorough records of your projects.
• Details about the data sources you consulted.
• Examples of the methods used in data modeling.

You can successfully showcase your proficiency and preparedness to work as a data engineer by developing a wide variety of data engineering projects.

Uncertain about where to begin? Here is a summary of seven significant data engineering projects that you can work on:

• End-to-End Batch ETL/ELT Data Pipeline. A reliable pipeline that uses Python, SQL, and fundamental ETL skills to extract, transform, and load data from several sources into a target system.
• Real-Time Streaming Pipeline. A project that uses tools like Real-Time Streaming Pipeline to handle data in motion, such as user events or data from Internet of Things sensors
• Cloud Data Warehouse & Lakehouse. Imagine designing a cloud-based data architecture that seamlessly blends a data lake and a warehouse, using platforms like AWS, Azure, or GCP. This shows you can navigate cloud environments, leverage big data tools, and optimize everything for both cost and performance.
• Generative AI Integration Pipeline. Think of an AI-driven project that weaves a large language model (LLM) or AI service into a data pipeline. This could involve prepping data for an LLM or using AI to enhance your data, showcasing that you’re right on trend with the latest in AI and LLM technology.
• Machine Learning Data Pipeline. Picture an end-to-end pipeline for a machine learning project, covering everything from data ingestion and feature engineering to model training and deployment. This demonstrates your ability to support AI and ML initiatives with strong data engineering practices.
• Multi-Source Data Ingestion. This project is all about collecting data from various external sources, like public APIs or through web scraping, then cleaning and integrating it into your database or data lake. It highlights your versatility in managing the diverse types of real-world data. (This is a fantastic starting point for beginners looking to dive into data engineering projects.)
• Automated Pipeline Deployment CI/CD & Docker. Here’s a “DevOps for data engineering” project where you’ll containerize a pipeline and set up CI/CD automation. This proves you can take your solutions to production using tools like Docker, Kubernetes, or GitHub Actions, ensuring everything runs smoothly and reliably.

Each of these projects helps you build skills in different facets of data engineering while also providing concrete evidence of your capabilities. Let’s explore each one in detail and discover why hiring managers are eager to see these in your portfolio!

1. End-to-End Batch Data Pipeline (ETL/ELT Project)

The foundation of data engineering is a traditional batch data pipeline project. This project involves extracting raw data from one or more sources (such as a SQL database, CSV exports, or JSON from an API), transforming it (cleaning and reshaping it), and then loading it into a data warehouse or other desired location. When employing contemporary warehouses that manage transformations, this is frequently referred to as an ELT (Extract-Load-Transform) or ETL pipeline (Extract-Transform-Load).

Batch pipelines are necessary for practically every business. Consider nightly tasks that compile user information from various apps into a central database or aggregate sales data for a retail business. In many positions, entry-level data engineers move and organize data so it’s ready for analysis. By creating an end-to-end ETL project, you’re doing the same thing. Employers are aware that you can likely assist with their production data workflows if you can create a strong ETL system for your portfolio.

This project lets you flex several core skills of a data engineer:

• SQL for querying and shaping data (for example, writing SQL transformations or designing the schema for the target database).
• Python (or another programming language) for writing scripts to extract and manipulate data. Python’s libraries, like pandas or SQLAlchemy, can be handy here.
• ETL Tools / Orchestration: You can use frameworks like Apache Airflow or Prefect to schedule and manage the pipeline. This shows you understand workflow orchestration (a skill mentioned in many job postings).
• Databases/Warehouses: Use a relational database or cloud data warehouse (e.g., PostgreSQL, Snowflake, BigQuery) as the destination. Designing your target schema (tables) for efficient querying is a big plus.
• Optionally, try using a big data framework like Apache Spark or a transformation tool like dbt if you’re dealing with large datasets or want to showcase advanced skills.

Every hiring manager has seen basic “I loaded a CSV” projects. To stand out, your pipeline should follow best practices from real production systems:

• Incorporate data validation, logging, and error handling. To make sure the data is clean and the schemas are consistent, for example, you may use a data quality tool (such as Great Expectations).
• Demonstrate your ability to intelligently update or attach new entries rather than reloading all the data every time (e.g., loading only yesterday’s data on a daily run).
• Provide a concise README along with a pipeline architecture diagram. Describe the flow of data from its source to its destination, mentioning any trade-offs or design choices that were made (this demonstrates system design thinking).
• Run the pipeline on a schedule using Airflow or cron tasks, and use alarms to track successes and errors. This proves that pipelines don’t require constant supervision.

Customer Analytics ETL Pipeline – Imagine an e-commerce company that needs to combine customer data from their app, website, and marketing database into a single warehouse table each day. You could build a pipeline that pulls data from three sources (say, a MySQL export, a REST API, and a cloud storage CSV), merges and transforms it (maybe standardizing customer IDs and cleaning up inconsistent entries), and loads it into a Snowflake or BigQuery table ready for the analytics team. By showcasing this project, you prove you can move and clean data at scale, which is exactly what a data engineer is hired to do.

2. Real-Time Streaming Data Pipeline

Events are continuously and instantly processed via a streaming data pipeline. You’re dealing with data that is continuously coming in rather than batch-loading it after a day or an hour. This project may involve processing a stream of events (such as user clicks on a website or readings from Internet of Things sensors) in real time and communicating the results to a database, dashboard, or alarm system. Commonly used technologies include cloud services like AWS Kinesis or GCP Dataflow, Apache Spark Streaming or Flink (for processing streams), and Apache Kafka (for streaming data intake and communications).

In 2025, an increasing number of businesses desire real-time insights. Consider ride-sharing apps that provide real-time driver location updates or fraud detection systems that flag questionable transactions as they occur. Real-time data pipelines are frequently needed by even contemporary analytics dashboards to ensure that metrics are continually current. Employers will see that you can manage the intricacy of real-time systems if you have a streaming project in your portfolio. This is a significant differentiator for you because many novices fail to see this.

This project will likely introduce some new tools into your stack:

• Kafka or Kinesis. for ingesting and transporting streaming data (Kafka is very popular in the industry for building pipelines, so using it in a project is a great resume booster).
• Stream Processing Framework, such as Spark Structured Streaming, Apache Flink, or even Python libraries like Faust. These tools let you write code that processes events continuously (e.g., aggregating events over a window of time, transforming or filtering streams).
• NoSQL or Time-Series Database. Many real-time use cases involve storing outputs in a fast datastore. For example, you might push processed data into a system like Cassandra, Redis, or a real-time analytics DB like InfluxDB or Druid, which are optimized for quick reads and writes.
• Websocket or Dashboard for Output. Optionally, you could build a simple live dashboard or use a tool like Apache Superset or Grafana to visualize the streaming data results in real time.

Real-Time Streaming Analytics for Logs – Set up a pipeline that consumes application logs or user activity events continuously via Kafka. Use Spark Streaming to calculate rolling metrics (say, number of logins per minute, or error rate per hour) and push those metrics to a live dashboard. Include an alerting mechanism (perhaps if the error rate goes above a threshold, trigger an alert). This project would show that you can build and orchestrate a pipeline that operates 24/7, which is exactly the challenge in systems like monitoring dashboards, stock price tickers, or IoT platforms.

3. Cloud Data Warehouse & Lakehouse Architecture

You create and execute a cloud-based data architecture in this project, which usually includes a data warehouse (an analytics database for quick queries) and a data lake (raw files on scalable cloud storage). In actuality, this can entail utilizing Snowflake, Amazon Redshift, or Google BigQuery as the data warehouse and services like Amazon S3 or Azure Data Lake Storage for the data lake. To transfer data into these systems and possibly maintain their updates, you will construct pipelines. The objective is to demonstrate how to employ cloud services to create a scalable data platform, which is sometimes referred to as a “lakehouse architecture” since it combines aspects of warehouses and lakes.

Both big and small businesses are making significant investments in cloud data platforms. Because of its scalability and managed infrastructure, cloud warehouses are either replacing or complementing legacy on-premise databases. Questions like “How can we put up our analytics on the cloud?” or “How do we handle our expanding data inexpensively but effectively?” are probably on the minds of hiring managers in 2025. You can demonstrate your ability to assist with modern data stack implementations and your understanding of cost-performance trade-offs by showcasing a project that makes use of a cloud platform.

Key technologies and components:

• Cloud Platforms. Pick one – AWS, Azure, or GCP – and use their data services. For example, on AWS you might use S3 (storage), Redshift (warehouse), and maybe AWS Glue or Lambda for pipeline tasks. On GCP, you might use Cloud Storage, BigQuery, and Dataflow. Knowing the basics of any one cloud is highly valuable (and much better than none).
• Data Warehouse. Snowflake (a popular independent cloud data warehouse) or the cloud-native ones mentioned (BigQuery, Redshift, Azure Synapse). Design some tables in the warehouse to serve a specific analytics purpose, applying data modeling techniques (star schema, snowflake schema, etc.) for efficiency and clarity.
• Data Lake & File Formats. Show that you can use a data lake for raw or big data. Use formats like Parquet or Avro for storing files in the lake for better compression and query performance. You might even implement a Delta Lake or Apache Iceberg on top of your cloud storage – these technologies enable ACID transactions and easier querying on data lakes (a big trend in 2025).
• Transformation Tools. Incorporate dbt (Data Build Tool) for transforming data in the warehouse. dbt is very popular in data engineering teams because it helps manage SQL transformations, testing, and documentation. Using it in your project shows you’re keeping up with modern best practices.
• Metadata and Governance. If you want to go the extra mile, mention or implement basic data cataloging or governance – e.g., use an open-source tool like Amundsen or Apache Atlas, or at least clearly document your data schemas. Data governance is increasingly important (especially in finance/healthcare industries), so showing awareness of it is a bonus.

Retail Analytics Lakehouse on AWS – Imagine a scenario where a retail chain collects sales transactions from stores and online. Build a lakehouse: raw data files go into an S3 data lake daily. An AWS Glue or Lambda job processes those files and loads aggregated, cleaned data into Amazon Redshift (the warehouse) tables designed in a star schema (e.g., a fact_sales table with dimension tables for store, product, date). Use dbt to run additional transformations and generate a few example reports (maybe total sales by region by month). You could even integrate a quick dashboard (using a tool like QuickSight or Tableau) on top of Redshift to show the end-to-end flow. By doing this, you demonstrate the ability to design a complete data architecture in the cloud – a skill highly sought after as businesses migrate their data infrastructure to AWS/Azure/GCP.

4. Generative AI Integration Pipeline (LLM Project)

Large language models (LLMs) and artificial intelligence (AI) will dominate 2025, unless you’ve been living off the grid. For a data engineer, however, what does that mean? You will demonstrate the relationship between data engineering and AI in this project. In essence, you create a pipeline that either uses an AI as part of the pipeline or feeds data into an AI/LLM. For instance, you could design a pipeline to gather and preprocess textual data (from product evaluations or service tickets, for instance), then import it into an LLM-powered application that offers summaries or insights. Alternatively, you might incorporate an LLM API (such as GPT-4 or a comparable model) into your process to do tasks like data classification or text-based metadata generation.

Key technologies/skills:

• LLM or AI API. You don’t need to train a model from scratch (that’s more of a data scientist’s job), but you can use a pre-trained model. For instance, use OpenAI’s API or Hugging Face transformers to do something with text data. Maybe you use an LLM to parse unstructured text into a structured form, or to generate summary tags for documents.
• NLP Libraries. Tools like spaCy or NLTK can help with text preprocessing (tokenization, cleaning, etc.) if your data is text-heavy.
• Vector Database or Embeddings. If your project involves semantic search or LLM context, you might generate embeddings (with a model like Sentence Transformers) and store them in a vector database (like FAISS, Pinecone, or an Elasticsearch index). This demonstrates knowledge of modern data storage for AI use-cases.
• Pipeline tools. You’ll still use Python (for the glue code and data prep) and maybe Airflow for orchestration, to show it’s a data pipeline at heart. Possibly include a small database to store results or a dashboard to show AI outputs, depending on the use case.
• Prompt engineering (optional). If using an LLM, designing effective prompts or fine-tuning the model on your data is a bonus skill to showcase. It shows you understand how to coax valuable results out of AI using the data you’ve prepared.

Support Ticket Summarization Pipeline – Build a pipeline that takes customer support emails or helpdesk tickets (you can find public datasets or create sample text), cleans and structures the text (using Python for NLP tasks), and then uses an LLM API to generate a brief summary or sentiment analysis for each ticket. Load the results into a small dashboard or database so that support managers can get insights like “What are the common issues this week?” This shows you can orchestrate data flow from raw text to an AI service and back to a useful output. You’d be demonstrating skills in data ingestion, text processing, calling an AI model, and handling the results – exactly the kind of pipeline that bridges data engineering and AI.

5. Machine Learning Data Pipeline (MLOps Project)

The data engineering aspect of machine learning is the focus of this project. Project 5 focuses on creating machine learning pipelines, whereas Project 4 briefly discussed integrating AI into a pipeline. In reality, data engineers frequently set up pipelines to deploy and track models as well as the infrastructure that gathers data, transforms it into features, and sends it for model training. A project focused on MLOps can entail developing a pipeline that receives new data, retrains a machine learning model, and publishes the updated model or its predictions. In essence, you’re using data in the loop to automate the lifecycle of an ML model.

Many companies don’t just do one-off ML models; they need continuous retraining and data refresh for their models. For instance, a recommendation system might retrain every week with the latest user data. Or an anomaly detection model might need new data feeding in daily. Hiring managers know that someone who’s comfortable with MLOps will be a huge asset to teams where data engineering and data science collaborate closely. It means you can help get models from the lab to production – often one of the hardest parts of launching AI products.

Key components/technologies:

• Feature Engineering Pipeline. Use tools like Apache Spark or pandas to transform raw data into features suitable for an ML algorithm. This could be a batch job or streaming process (for real-time predictions).
• Model Training Automation. Use a library like scikit-learn, TensorFlow, or PyTorch to train a model within your pipeline (or trigger a training job in a cloud ML service). Even if the model is simple, the focus is on automating the training process with fresh data.
• Model Serving. Show how you’d deploy the model. This could be as simple as saving a model file and loading it in a Flask API to serve predictions, or using a more robust solution like Docker + a model server (TensorFlow Serving, TorchServe) or an AWS SageMaker endpoint.
• Scheduling & Automation. Use Airflow or similar to orchestrate an end-to-end flow: for example, a daily job that prepares data, retrains the model, evaluates it, and if it meets certain metrics, deploys it. Include monitoring steps if possible (like if model performance drops, trigger an alert or retrain more often).
• Data/Model Versioning. If you can, use tools like DVC (Data Version Control) or MLflow to track dataset versions, model parameters, and evaluation metrics. This is advanced but very impressive as it shows you understand the importance of reproducibility in ML pipelines.

6. Multi-Source Data Ingestion Project (APIs & Web Scraping)

Not all of the information you require is conveniently included in a single file or database. Data engineers frequently have to gather and integrate data from a variety of external sources, such as online services, APIs, and scraped websites. You will build a pipeline in this project that gathers data from multiple sources, potentially on different schedules, and transforms it into a format that can be used. For instance, in a single workflow, you may retrieve CSV files from an FTP server, scrape a website for rival prices, and use a public API for weather data.

Companies love it when an engineer can “get the data we need, wherever it is.” Maybe marketing needs data from a third-party service, or you have to combine open data with internal data to enrich your company’s insights. Showing that you can handle APIs (including authentication, rate limits, and JSON data) and web scraping (parsing HTML) proves that you’re resourceful and versatile. It’s also an area where many newcomers struggle, so having a project like this can differentiate you from other beginners who stick to just one data source.

Key technologies/skills:

• APIs and JSON. Using Python libraries like requests to call REST APIs, handling JSON or XML responses. You’ll show you can paginate through results, handle API rate limits or errors, and parse nested data structures.
• Web Scraping. Utilizing tools like BeautifulSoup or Scrapy to crawl web pages and extract information. This shows comfort with unstructured data (HTML) and how to turn it into structured data.
• Data Cleaning. External data is often messy. Demonstrating that you can clean and standardize it (maybe one API gives dates in UTC and another in local time, or different units that you normalize) is key.
• Data Merge & Storage. Once you have data from multiple sources, you likely need to merge it together. For instance, combining weather data with your sales data on matching dates, or aggregating different sources into one table for analysis. Use a database or even just pandas to merge, and then load into a final storage (database table or a CSV) for use.
• Scheduling/Orchestration. If different sources need to be fetched at different intervals (e.g., one API updates daily, another hourly), use Airflow or separate cron jobs to manage these tasks, and then a final step to join everything. This shows you can coordinate complex workflows with multiple inputs.

7. Automated Pipeline Deployment (CI/CD & Docker DevOps)

Making any pipeline you’ve constructed production-grade is the goal of this final project idea, rather than creating a brand-new kind of data pipeline. The DevOps aspect of data engineering will be your main focus here. You will use Docker to containerize your pipeline code and configure Continuous Integration/Continuous Deployment (CI/CD) to test and deploy your pipeline automatically. In essence, you approach your data pipeline as though it were a software project that must be completed with reliability. For instance, you might test your pipeline code using GitHub Actions or Jenkins (maybe with a tiny test on sample input data) and then deploy the pipeline (or update a scheduler) each time you post changes. This can also involve provisioning the resources your pipeline needs using Infrastructure as Code technologies like Terraform.

Key technologies/skills:

• Docker: Containerize your application – for instance, package your ETL script and its dependencies into a Docker image. This shows you understand containerization, which is useful for both local testing and cloud deployment.
• CI/CD Pipeline: Use a platform like GitHub Actions, GitLab CI, Jenkins, or CircleCI to define a pipeline that runs on every code commit. This could lint your code, run any automated tests (maybe you test a small ETL job on sample data), and then deploy. “Deploy” might mean pushing the Docker image to a registry and triggering the pipeline in production (e.g., updating an Airflow DAG or a cloud function).
• Kubernetes or Cloud Services (optional): If you want to show off, deploy your Docker container to a Kubernetes cluster or a serverless container service (like AWS ECS/Fargate or GCP Cloud Run). This is not required, but if you do it, you’re demonstrating cutting-edge deployment skills.
• Infrastructure as Code (optional): Using Terraform or CloudFormation to spin up, say, an AWS Lambda, an S3 bucket, or a database for your pipeline. This indicates you can automate environment setup – a big plus for reliability and consistency.

From Portfolio to Paycheck: Make Your Projects Count

Having these projects in your arsenal is like holding a master key to unlock a data engineering job. Each project honors a different facet of what companies need:

• Pipeline fundamentals (Project 1) – proving you can move and transform data reliably.
• Real-time processing (Project 2) – showing you handle high-velocity data and complex systems.
• Cloud and big data architecture (Project 3) – demonstrating you can design scalable solutions and optimize them for performance and cost.
• AI/ML integration (Projects 4 & 5) – highlighting that you’re ready for the cutting-edge and can support data science teams.
• Data sourcing and integration (Project 6) – indicating you’re versatile and resourceful with data in the wild.
• DevOps and polish (Project 7) – assuring employers that you write production-ready code that’s maintainable and automated.

Remember, it’s not about doing all of these at once or overnight. Pick one or two that align with the kind of role you want and start there. Build gradually – quality matters more than quantity. A well-documented, well-executed project where you learned something deeply will impress more than seven half-baked tutorials.

Most importantly, be ready to talk about your projects. In interviews, you’ll likely be asked about challenges you faced, design decisions you made, and what you’d do differently with more time. This is where your genuine understanding will shine through. The fact that you’ve actually built these systems means you can confidently discuss them, which instantly sets you apart from candidates who only know theory.

To get inspired and learn how the process worked for others, you can watch our video feedback from those who have already gone through the process and found a job. Hearing real success stories can spark ideas and keep you motivated as you work on your own portfolio.

By following this roadmap of projects, you’re not just ticking boxes for a resume – you’re practicing the real job. And that’s exactly what hiring managers want to see: someone who can hit the ground running. With each project, you’ll grow more comfortable with the tools and more fluent in the language of data engineering. Before you know it, you’ll be discussing how you built a streaming pipeline or deployed an ML model in an interview – and that’s the kind of talk that turns interviews into offers.

Good luck on your journey from portfolio to paycheck! And remember, every data engineering expert started with that first project, so stay curious and keep building.

For hands-on guidance in creating these portfolio projects, you can also enroll in specialized data engineering courses. For example, Data Engineer Academy is our mentorship-driven data engineering academy program that walks you through projects step by step. Structured guidance like this can dramatically speed up your learning curve and ensure you’re applying best practices.

Ready to build these projects? See our courses:

FAQ: Data Engineering Projects and Career Growth

Q. Why do I need a project portfolio if I already have technical skills?
A portfolio bridges the gap between theory and practice. It shows employers that you can apply your skills to build production-level solutions, making you a stronger candidate than someone with only coursework or certifications.

Q. How many projects should I include in my portfolio?
Quality is more important than quantity. Two or three well-documented, end-to-end projects are often enough to demonstrate your skills. Focus on clarity, scalability, and business relevance rather than rushing through multiple unfinished projects.

Q. Which tools and technologies should I use for these projects?
Prioritize widely adopted tools such as Python, SQL, Spark, Airflow, and cloud platforms like AWS, Azure, or GCP. For AI/ML pipelines, consider TensorFlow, PyTorch, or integration with large language models (LLMs). Always align your stack with what’s in demand in the roles you’re targeting.

Q. Do hiring managers prefer academic or real-world project examples?
Real-world or production-like projects resonate more strongly. For instance, building a streaming pipeline for real-time user events or a multi-source ingestion project shows problem-solving skills that mirror actual company challenges.

Q. How should I present my projects to employers?
Use GitHub or similar platforms to host your portfolio. Include:

• Clear documentation and READMEs
• Architecture diagrams
• Explanations of your data sources and modeling methods
• Results, visualizations, or dashboards that highlight the business impact

Q. What’s the best project to start with as a beginner?
Beginners should start with data ingestion and transformation projects or an ETL/ELT pipeline. These build strong foundational skills that make advanced projects like machine learning pipelines or AI integration much easier to tackle later.

Q. How can I keep my portfolio up to date?
Continuously improve your projects by:

• Adding new data sources
• Refactoring for performance
• Integrating the latest tools (e.g., AI/LLM, cloud-native services)
• Documenting lessons learned

Keeping your portfolio fresh signals that you’re an adaptable engineer who’s committed to growth.

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.