The Only Data Engineering Roadmap You Need to Build a Killer Portfolio (Plus an AI Bonus)

Written By Chris Gambill

1. The Data Engineering Dilemma

You’ve probably been there: jumping between tutorials, juggling tools, and wondering if you’re learning the right thing. Data engineering can feel like trying to map a city while lost in the fog.

Here’s the truth: over a million people enrolled in data engineering courses on Udemy last year, but only a small fraction finished, and fewer still built anything that could get them hired.

This roadmap changes that.

After 25 years building pipelines from SQL Server to Databricks, I can tell you that clarity comes from structure. These are the five courses I’d take if I were starting from zero. Each one ends with a project you can build, explain, and add to your portfolio with pride.

If you’ve been stuck in tutorial hell, this is your exit ramp.

2. Phase 1: The Foundation — Thinking in Sets (SQL)

Before you touch Spark, Python, or cloud tools, start with the one skill that every data engineer shares: SQL. It’s the shovel that moves your data.

Course: The Complete SQL Bootcamp

This course teaches you how to query like an engineer, not a spreadsheet user. You’ll learn how to filter, join, and calculate efficiently.

The Project:
Rebuild a small e-commerce analytics database. Create tables for orders, customers, and products, then write queries to calculate total sales, repeat customers, and top sellers.
Document your results like you’re showing them to a real stakeholder. Add visuals, context, and your reasoning.

Interview Edge:
“How do you identify and fix duplicate records in SQL?”
If you can explain multiple methods using grouping, window functions, and CTEs, you’re already above average.

Pro Tip:
Redo every exercise from memory. That’s how you move from knowing to mastering.

Rehook: Once you can query data confidently, you’ll start hitting a wall with scale. That’s when it’s time to think bigger and learn Spark.

3. Phase 2: Mastering Scale — Processing Huge Data (Python and Spark)

Once you understand SQL, you’ll notice that real-world data rarely fits neatly into one machine. This is where Spark becomes your best friend.

Course: Databricks and PySpark for Big Data: From Zero to Expert

This course turns Python into your data engine. You’ll learn distributed processing, transformations, and performance tuning on Databricks.

The Project:
Use Databricks Community Edition and the NYC Taxi dataset to build a PySpark aggregation job. Group trips by date and location, calculate average fares, and store your results in Delta format.

Interview Edge:
“What’s the difference between a wide and narrow transformation in Spark, and why does it matter?”
If you can explain shuffling and how Spark minimizes it, you’re thinking like an engineer who understands performance.

Pro Tip:
Master lazy evaluation. It’s the secret to Spark’s power.

Rehook: Now that you know how to handle data at scale, you need to learn how to organize and govern it. This is where Databricks takes center stage.

4. Phase 3: Organization and Governance — The Modern Lakehouse

Data engineering isn’t just about moving data fast. It’s about making it reliable, structured, and secure.

Course: Databricks Certified Data Engineer Associate

This course teaches you the backbone of modern data systems: Delta Lake, Unity Catalog, and the Medallion Architecture. It’s how you turn chaos into clarity.

The Project:
Build a mini Medallion pipeline:

  • Bronze layer for raw CSVs

  • Silver layer for cleaned and standardized data

  • Gold layer for aggregated dashboards

Interview Edge:
“What is the Medallion Architecture, and how does Delta Lake enforce reliability across its layers?”
When you can answer this clearly, you show that you understand design, not just code.

Pro Tip:
You don’t need enterprise access. Use the free Databricks Community Edition to build your own clusters and pipelines.

Rehook: You’ve built structure and governance. Now it’s time to make your data come alive automatically with Airflow and ETL orchestration.

5. Phase 4: Automation — Orchestrating the Flow (Airflow and ETL)

Now that your data is trustworthy, it’s time to make it move on its own. This is where orchestration turns theory into production.

Course: The Complete Data Engineering Bootcamp with PySpark (2025)

You’ll learn how to schedule and monitor ETL jobs using Airflow, Python, and Spark.

The Project:
Deploy Airflow using Docker. Build a daily DAG that loads CSV data, transforms it with PySpark, and writes it to Delta. Add a sensor and a failure alert so your pipeline never runs blind.

Interview Edge:
“How would you design a data pipeline to handle late-arriving data or upstream failures?”
That’s the kind of question that separates entry-level from professional.

Pro Tip:
Make the project yours. Change logic, rename tables, and add meaningful logging. Employers want to see your fingerprints on the work.

Rehook: You’ve automated your pipelines. Now it’s time to connect all the moving parts into one end-to-end system that mirrors a real production environment.

6. Phase 5: Production Readiness — The End-to-End System

This is where everything you’ve learned connects: ingestion, transformation, streaming, and delivery.

Course: Data Engineering Vol. 2 AWS: Data Processing - Spark and Kafka

This course ties together Spark, Kafka, and AWS into a fully functioning data platform. It’s the big leagues — the kind of work that gets you hired.

The Project:
Extend your existing pipeline to process streaming data. Simulate real-time events (like Twitter or IoT data), process it with Spark Streaming, store it in S3 or ADLS, and visualize it with Power BI or Tableau.

Interview Edge:
“How would you design a real-time data pipeline using Kafka and Spark Streaming?”
If you can talk through ingestion, transformation, and checkpointing, you’ll hold the interviewer’s attention.

Pro Tip:
Document everything. Screenshots, readme files, even short video demos. Your ability to communicate your process is your superpower.

Rehook: You’ve built data systems that run like clockwork. The next frontier is using that data to build intelligence — AI engineering.

7. Bonus Track: The Future is AI Engineering

If you’ve made it this far, you already have the foundation most engineers never reach. Now you can start applying it to the next big wave: AI.

Course: The AI Engineer Course 2025: Complete AI Engineer Bootcamp

This course connects data engineering with AI. You’ll learn how to use LangChain, vector databases, and transformer models to integrate data pipelines with large language models and speech-to-text systems.

Rehook: AI is not replacing data engineers. It’s amplifying the ones who know how to feed it good data. Be that engineer.

8. Your Next Steps

You now have a complete roadmap — five courses, five projects, and one AI bonus that future-proofs your career.

But remember, courses don’t build careers. Projects do.

Start small, finish something, and document it like your future self will thank you. Once you’ve built your first system, watch my next video, “How to Build Your First Data Engineering Project in 30 Days.” It will show you how to bring everything together into a single, professional-grade portfolio piece.

All of the courses listed here are linked in my YouTube description. Those are affiliate links that help support my channel at no extra cost to you — and every click helps me keep creating resources like this for free.

If this article gave you clarity, drop a few claps. It tells me to keep writing more guides that help data engineers cut through the noise and focus on what matters.

You’re not just learning data engineering. You’re building a foundation for a career that creates order out of chaos.

1. The Data Engineering Dilemma

You’ve probably been there: jumping between tutorials, juggling tools, and wondering if you’re learning the right thing. Data engineering can feel like trying to map a city while lost in the fog.

Here’s the truth: over a million people enrolled in data engineering courses on Udemy last year, but only a small fraction finished, and fewer still built anything that could get them hired.

This roadmap changes that.

After 25 years building pipelines from SQL Server to Databricks, I can tell you that clarity comes from structure. These are the five courses I’d take if I were starting from zero. Each one ends with a project you can build, explain, and add to your portfolio with pride.

If you’ve been stuck in tutorial hell, this is your exit ramp.

2. Phase 1: The Foundation — Thinking in Sets (SQL)

Before you touch Spark, Python, or cloud tools, start with the one skill that every data engineer shares: SQL. It’s the shovel that moves your data.

Course: The Complete SQL Bootcamp

This course teaches you how to query like an engineer, not a spreadsheet user. You’ll learn how to filter, join, and calculate efficiently.

The Project:
Rebuild a small e-commerce analytics database. Create tables for orders, customers, and products, then write queries to calculate total sales, repeat customers, and top sellers.
Document your results like you’re showing them to a real stakeholder. Add visuals, context, and your reasoning.

Interview Edge:
“How do you identify and fix duplicate records in SQL?”
If you can explain multiple methods using grouping, window functions, and CTEs, you’re already above average.

Pro Tip:
Redo every exercise from memory. That’s how you move from knowing to mastering.

Rehook: Once you can query data confidently, you’ll start hitting a wall with scale. That’s when it’s time to think bigger and learn Spark.

3. Phase 2: Mastering Scale — Processing Huge Data (Python and Spark)

Once you understand SQL, you’ll notice that real-world data rarely fits neatly into one machine. This is where Spark becomes your best friend.

Course: Databricks and PySpark for Big Data: From Zero to Expert

This course turns Python into your data engine. You’ll learn distributed processing, transformations, and performance tuning on Databricks.

The Project:
Use Databricks Community Edition and the NYC Taxi dataset to build a PySpark aggregation job. Group trips by date and location, calculate average fares, and store your results in Delta format.

Interview Edge:
“What’s the difference between a wide and narrow transformation in Spark, and why does it matter?”
If you can explain shuffling and how Spark minimizes it, you’re thinking like an engineer who understands performance.

Pro Tip:
Master lazy evaluation. It’s the secret to Spark’s power.

Rehook: Now that you know how to handle data at scale, you need to learn how to organize and govern it. This is where Databricks takes center stage.

4. Phase 3: Organization and Governance — The Modern Lakehouse

Data engineering isn’t just about moving data fast. It’s about making it reliable, structured, and secure.

Course: Databricks Certified Data Engineer Associate

This course teaches you the backbone of modern data systems: Delta Lake, Unity Catalog, and the Medallion Architecture. It’s how you turn chaos into clarity.

The Project:
Build a mini Medallion pipeline:

  • Bronze layer for raw CSVs

  • Silver layer for cleaned and standardized data

  • Gold layer for aggregated dashboards

Interview Edge:
“What is the Medallion Architecture, and how does Delta Lake enforce reliability across its layers?”
When you can answer this clearly, you show that you understand design, not just code.

Pro Tip:
You don’t need enterprise access. Use the free Databricks Community Edition to build your own clusters and pipelines.

Rehook: You’ve built structure and governance. Now it’s time to make your data come alive automatically with Airflow and ETL orchestration.

5. Phase 4: Automation — Orchestrating the Flow (Airflow and ETL)

Now that your data is trustworthy, it’s time to make it move on its own. This is where orchestration turns theory into production.

Course: The Complete Data Engineering Bootcamp with PySpark (2025)

You’ll learn how to schedule and monitor ETL jobs using Airflow, Python, and Spark.

The Project:
Deploy Airflow using Docker. Build a daily DAG that loads CSV data, transforms it with PySpark, and writes it to Delta. Add a sensor and a failure alert so your pipeline never runs blind.

Interview Edge:
“How would you design a data pipeline to handle late-arriving data or upstream failures?”
That’s the kind of question that separates entry-level from professional.

Pro Tip:
Make the project yours. Change logic, rename tables, and add meaningful logging. Employers want to see your fingerprints on the work.

Rehook: You’ve automated your pipelines. Now it’s time to connect all the moving parts into one end-to-end system that mirrors a real production environment.

6. Phase 5: Production Readiness — The End-to-End System

This is where everything you’ve learned connects: ingestion, transformation, streaming, and delivery.

Course: Data Engineering Vol. 2 AWS: Data Processing - Spark and Kafka

This course ties together Spark, Kafka, and AWS into a fully functioning data platform. It’s the big leagues — the kind of work that gets you hired.

The Project:
Extend your existing pipeline to process streaming data. Simulate real-time events (like Twitter or IoT data), process it with Spark Streaming, store it in S3 or ADLS, and visualize it with Power BI or Tableau.

Interview Edge:
“How would you design a real-time data pipeline using Kafka and Spark Streaming?”
If you can talk through ingestion, transformation, and checkpointing, you’ll hold the interviewer’s attention.

Pro Tip:
Document everything. Screenshots, readme files, even short video demos. Your ability to communicate your process is your superpower.

Rehook: You’ve built data systems that run like clockwork. The next frontier is using that data to build intelligence — AI engineering.

7. Bonus Track: The Future is AI Engineering

If you’ve made it this far, you already have the foundation most engineers never reach. Now you can start applying it to the next big wave: AI.

Course: The AI Engineer Course 2025: Complete AI Engineer Bootcamp

This course connects data engineering with AI. You’ll learn how to use LangChain, vector databases, and transformer models to integrate data pipelines with large language models and speech-to-text systems.

Rehook: AI is not replacing data engineers. It’s amplifying the ones who know how to feed it good data. Be that engineer.

8. Your Next Steps

You now have a complete roadmap — five courses, five projects, and one AI bonus that future-proofs your career.

But remember, courses don’t build careers. Projects do.

Start small, finish something, and document it like your future self will thank you. Once you’ve built your first system, watch my next video, “How to Build Your First Data Engineering Project in 30 Days.” It will show you how to bring everything together into a single, professional-grade portfolio piece.

All of the courses listed here are linked in my YouTube description. Those are affiliate links that help support my channel at no extra cost to you — and every click helps me keep creating resources like this for free.

If this article gave you clarity, drop a few claps. It tells me to keep writing more guides that help data engineers cut through the noise and focus on what matters.

You’re not just learning data engineering. You’re building a foundation for a career that creates order out of chaos.

Chris Gambill
Next

Master Data Engineering: 7 Books (and a Roadmap)