Data Engineering Interview Tutorial with Examples.
Data engineering interview tutorial: SQL and Python rounds, pipeline design, data modelling, Spark, streaming and take-home projects.
Beginner
Start here — no prior experience needed
The Data Engineering Interview
What the five rounds actually test, and a worked warm-up question answered two ways — the version that gets marked down and the version that gets an offer.
The SQL Round
The twelve patterns that cover almost every SQL interview question — top-N per group, running totals, and gaps and islands, wrong answer first.
The Python Round
Data manipulation questions answered in plain Python and in pandas — with the O(n²) answer that passes on ten rows and times out on a million.
Pipeline Design Questions
The round that separates mid from senior — requirements first, then a design whose failure modes you name before the interviewer does.
Data Modelling Questions
Grain, keys and star schemas — plus the slowly changing dimension question, worked until the wrong join silently restates last quarter's revenue.
Intermediate
For developers with core concepts down
Spark and Distributed Systems Questions
Shuffles, skew and broadcast joins — why one task runs for 40 minutes while 199 finish in seconds, and the four fixes ranked by what interviewers expect.
Streaming and CDC Questions
Exactly-once explained without hand-waving, the offset commit order that loses data, and the watermark that silently drops 42% of revenue.
Data Quality and Incident Questions
The round about the day something went wrong — structuring an incident answer and finding a discrepancy by bisecting the pipeline.
Advanced
Production-grade patterns for experienced engineers
Data System Design
The 45-minute whiteboard round, sequenced — requirements, back-of-envelope sizing, storage choice, and the tradeoffs you volunteer before being asked.
The Take-Home Project
What is actually marked — a repo skeleton, the README that does most of the work, and the four hours' effort that beats forty.