Data Engineer System Design Interview Questions

Usually yes. Expect an architecture walkthrough (whiteboard/diagram) and sometimes a short SQL/Python exercise or a “debug this pipeline” prompt.

Absolutely. You’ll discuss past projects, incident handling, stakeholder alignment (analysts, ML, product), and how you prioritize reliability vs. speed vs. cost.

Depth over breadth. Interviewers look for concrete choices on batch vs. streaming, storage formats, partitioning, orchestration, backfills, SLAs, and monitoring—not just name-dropping tools.

Crisp communication, requirements gathering, and collaboration. Can you translate business needs into data contracts and negotiate SLAs with downstream teams?

Be prepared to explain the sources-to-sinks flow and when you’d use CDC versus batch; choose between streaming and micro-batch while justifying guarantees like exactly once. Briefly defend your storage (lake vs. warehouse), file formats (Parquet/Avro), and partitioning/clustering; outline orchestration with retries, backfills, and schema evolution; and show how you enforce data quality, track lineage, set up observability, and commit to clear SLAs.

Clarify goals & SLAs → estimate volumes/freshness → choose batch/stream → design storage & schema → plan transforms & orchestration → address quality/lineage → cover failures, backfills, and cost → summarize trade-offs.

Tools help, fundamentals win. Map requirements to primitives (queue, stream, batch job, columnar storage, DAG). If you don’t know a tool, describe the pattern and an equivalent.

Primarily architecture, with occasional SQL (joins, windows, aggregations) or Python snippets for transforms and tests. Clarity and correctness beat cleverness.

Ignoring data volume/skew, no plan for late/duplicate events, hand-waving backfills, skipping schema evolution, no data quality gates, and failing to discuss observability or cost.

Often. You may triage a slow job (skew, shuffles), fix a broken partition strategy, make a pipeline idempotent, or add quality checks to catch null explosions.

Contrast batch vs. stream, hot vs. cold storage, columnar vs. row, freshness vs. cost, and exactly-once vs. at-least-once. Tie choices back to SLAs and downstream consumers.

Rehearse 3–4 canonical designs (event ingestion, CDC to lake/warehouse, streaming enrichment, analytics mart). Time-box a 30–40 min “design loop,” draw diagrams, and narrate assumptions, numbers, and failure modes.