Interview Questions for a Data Engineer
Interview a data engineer by probing how they design reliable ELT pipelines, model data in the warehouse, and guarantee data quality. Move from concrete SQL and dbt decisions to orchestration, cost optimization, and incident handling, so you can assess whether they build trustworthy, observable, and cost-aware data platforms rather than brittle one-off scripts.
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Run this interview as a mix of design discussion and hands-on probing rather than algorithm puzzles. Ask candidates to walk through a pipeline they actually shipped, then push on the trade-offs they made around modeling, testing, and cost. Strong candidates think in terms of idempotency, lineage, data contracts, and observability, and can explain failures and what they changed afterward.
Technical & Role-Specific
What to look for: Distinguishes extraction/load from transformation, mentions tools like Fivetran/Airbyte or Kafka for ingestion, dbt for transformation, and reasons about incremental loads, idempotency, and handling schema drift from each source.
What to look for: Ties the choice to query patterns, BI tool behavior, warehouse cost, and consumer needs rather than dogma; explains where denormalization helps and where conformed dimensions matter.
What to look for: References incremental materialization, a unique key, merge/upsert strategy, lookback windows for late data, and dbt tests on uniqueness and freshness to catch regressions.
What to look for: Profiles expensive queries, looks at clustering/partitioning, warehouse sizing and auto-suspend, materialization choices, scanned bytes, and avoiding full refreshes where incremental works.
What to look for: Covers dbt tests, Great Expectations or equivalent, freshness and volume checks, anomaly alerting, and observability tooling like Monte Carlo, plus where alerts route and who owns them.
What to look for: Discusses deterministic transformations, atomic swaps or staging tables, deduplication on natural keys, and orchestration retries that don't double-load data.
Behavioral & Past Experience
What to look for: Honest ownership, root-cause analysis, a concrete prevention (test, contract, alert) added afterward, and clear communication to downstream consumers.
What to look for: Shows working with source-system owners, building tolerant ingestion, schema-change detection, and contracts or alerting so silent breakages surface early.
What to look for: Describes the motivation (cost, scale, reliability), backfill and parallel-run strategy, validation against the old system, and rollback planning.
What to look for: Explains building reliable feature/serving pipelines, scheduling, monitoring for drift or staleness, and a clear handoff contract rather than a thrown-over-the-wall script.
What to look for: Identifies a repetitive manual task, the automation built, and measurable time saved or errors reduced, showing a bias toward leverage.
Situational & Problem-Solving
What to look for: Reproduces against source-of-truth, checks lineage from dashboard back through models, validates tests actually cover the affected logic, and questions whether 'green' means 'correct.'
What to look for: Chunked or partitioned backfill, off-peak scheduling, separate warehouse sizing, validation of row counts and aggregates, and keeping the incremental run untouched during backfill.
What to look for: Probes the real business need, weighs streaming/micro-batch against complexity and cost, and proposes the simplest architecture that meets the actual freshness requirement.
What to look for: Mentions role-based access, masking or row/column-level security, separating raw from curated layers, and least-privilege IAM in the warehouse.
What to look for: Establishes a data contract or versioning, sets up alerting on the change, plans a compatibility window, and coordinates the migration with consumers rather than reacting after breakage.
Collaboration & Culture
What to look for: Uses dbt docs, a data catalog, clear model descriptions and ownership, and treats documentation as part of delivery rather than an afterthought.
What to look for: Seeks a single source of truth, drives toward a documented, tested definition, and involves the right business owner rather than maintaining two conflicting versions.
What to look for: Frames work around consumer impact and reliability, communicates SLAs and incidents clearly, and partners with stakeholders on roadmap trade-offs.
What to look for: Values reviewing SQL/dbt for correctness, tests, and maintainability, shared style and modeling conventions, and CI that runs tests before merges.
Data Engineer interview scorecard
Score every candidate on the same criteria, immediately after the interview, using evidence you actually heard rather than an overall impression. Agree the criteria with the panel before the first interview — deciding what counts after you have met people is how the loudest interviewer wins the debrief.
| Criterion | Evidence to record | Score 1-5 |
|---|---|---|
| Technical & Role-Specific | What the candidate actually said or did, in their own example — not your impression of it | 1 2 3 4 5 |
| Behavioral & Past Experience | What the candidate actually said or did, in their own example — not your impression of it | 1 2 3 4 5 |
| Situational & Problem-Solving | What the candidate actually said or did, in their own example — not your impression of it | 1 2 3 4 5 |
| Collaboration & Culture | What the candidate actually said or did, in their own example — not your impression of it | 1 2 3 4 5 |
| Overall recommendation | Strong no / no / mixed / yes / strong yes, with the single reason that decided it | - |
Want this as a reusable document? Use the interview scorecard template.
Questions to avoid asking a Data Engineer
Exactly which questions are unlawful depends on where you are hiring, and the rules change — so treat this as the list of topics to route through your own employment counsel, not as a legal standard. The practical test that holds everywhere: if the answer could not change how the person does this job, you have no reason to ask it.
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