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Data engineering is where a lot of "senior" titles hide people who have only ever written SQL against a warehouse someone else built. The pool that can actually design a pipeline, pick sane storage, and reason about cost is much thinner than the resumes suggest. The real risk is hiring someone fluent in the tools (Airflow, Spark, dbt) who has never owned the ugly parts: late-arriving data, schema drift, and a backfill that has to run without double-counting. Pin down whether you need pipeline plumbing, warehouse modeling, or streaming, because those are three different people more often than not.
Look for someone who thinks about idempotency and data quality before they name a tool. A strong candidate can explain how they make a job safe to re-run, how they catch bad data before it reaches a dashboard, and what they do about a source that silently changes its schema. Ask for a real story about a pipeline that broke in production: a backfill gone wrong, a partition that blew up costs, or a metric that was quietly wrong for a week before anyone noticed. The tell is whether they talk about correctness and recovery, or just which framework they wired together.
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Post a roleEngineering Manager | Solution Architect | Platform Engineering Leader
AI Solutions Implementation Lead and senior software engineer
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