Data Engineer Career Path
A complete, free path toward a data engineering role — Python foundations, then the techniques for processing data far larger than memory, all on real open datasets. No cloud required.
At a glance
Data engineers build the pipelines that move and shape data before anyone analyzes it. This path starts with solid Python and then teaches the craft of working with data too big for memory — measuring and shrinking it, processing it in chunks, parallelizing it, and structuring it for speed — all on the real, public New York City taxi dataset. Dedicated courses on distributed processing with Spark, containerization with Docker and Kubernetes, and pipeline orchestration with Airflow are being added to complete the path.
What you'll learn
- Python programming from scratch
- Why data outgrows memory — and how to measure it
- Memory-efficient NumPy and pandas at scale
- Chunked and out-of-core processing with SQLite
- Parallel processing across CPU cores
- The data structures and indexes real pipelines rely on
Your learning path
Work through the phases in order. Each card is a full course you can start right now.
Phase 1 — Python Foundations
Start here even with zero programming experience. Already fluent? Skip to Phase 2.
Phase 2 — Scaling Data with Python
Where data engineering begins: handling data that's too big to fit in memory, in pure Python.
Where this path can take you
Skills from this path map directly to roles like:
- Data Engineer
- Analytics Engineer
- ETL Developer
- Data Platform Engineer
- ML Platform Engineer
No hidden costs, no premium tiers
Build real projects for your portfolio
Learn at your own speed, no deadlines
Ready to start your Data Engineering journey?
Begin with the first course and work through the complete path at your own pace.
Start the first lesson