A hiring manager's question bank for data engineers — pipelines, modeling, SQL and Python, Spark, and the data-quality instincts that keep dashboards honest. Use these to find someone who builds systems that survive bad input.
A data engineer is judged by the pipelines nobody notices — the ones that run every night, recover from a malformed file, and never quietly drop half the rows. So while a candidate must be able to write a correct join and explain a partition strategy, the deeper signal is whether they design for failure. Real production data is late, duplicated, schema-drifted, and occasionally garbage; the engineers worth hiring assume this and build idempotent, observable systems around it. The questions below move from foundations (SQL fluency, batch versus streaming, dimensional modeling) into the harder ground of pipeline design and data quality. Use the early questions to confirm they can actually move and shape data, then spend the bulk of the interview on scenarios: how they would backfill three months of history without double-counting, what they do when an upstream API silently changes a field type, how they decide between ELT in the warehouse and transformation in Spark. Reward candidates who talk about idempotency, late-arriving data, schema evolution, and monitoring without being prompted — those are the habits that separate someone who has shipped pipelines from someone who has only read about them. Watch for engineers who confuse "the job succeeded" with "the data is correct," and favor those who instrument their own pipelines so they find problems before the business does.
Confirm core SQL and modeling fluency with two or three foundational questions, then spend most of the interview on a pipeline-design scenario specific to your stack. The strongest signal is an engineer who designs for late, duplicate, and malformed data without being asked.
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