A data engineer builds the pipes the data flows through before any analyst or model touches it. It is a less glamorous title than data scientist, and considerably more in demand in the real market.
Stage one: SQL before anything else
Do not start with Python. Start with SQL and learn it properly: window functions, nested queries, reading an execution plan and understanding why a query slows down.
Most applicants for data engineering roles are filtered out on the SQL test, not on advanced architecture questions.
Stage two: Python for pipelines
You do not need advanced Python. You need to write clean code that reads from a source and writes to a destination, and handles errors instead of collapsing at the first odd row.
The key skill here is not algorithms — it is handling failure: what happens when the connection drops halfway through a transfer?
Stage three: modelling and organisation
Learn warehouse design: star schemas, fact and dimension tables, and the difference between raw, processed and consumption-ready data.
This is where the gap opens between someone who moves data and someone who engineers it.
Stage four: operations
Scheduling, monitoring, alerting and documentation. A pipeline nobody knows has stopped is worse than no pipeline at all.
Learn one scheduling tool well, and build the habit of writing an alert for every step that can fail.
How long does this realistically take?
With serious part-time commitment: twelve to eighteen months to reach an entry-level hireable standard. Any promise of less ignores that skill is built by repetition, not by watching.
More important than the duration: build three real projects whose decisions you can defend in an interview. A project where you can justify every choice is worth ten certificates.
