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Describe a data pipeline you designed from scratch. What were the requirements, what architecture decisions did you make, and what challenges did you solve?

Strong candidates walk through the full lifecycle: source system characteristics, ingestion pattern (batch vs. streaming), transformation logic, data model design, orchestration, and monitoring. Look for deliberate decisions — why Kafka over Kinesis, why star schema over flat table — rather than just a description of what they built. Engineers who can't articulate tradeoffs are following patterns rather than understanding them.

How do you ensure data quality and consistency across multiple sources with different schemas, formats, and update frequencies?

Look for a layered approach: schema validation at ingestion, data contracts or expectations (Great Expectations, dbt tests), row count and null checks at each pipeline stage, and alerting for anomalies. Strong candidates describe what happens when data quality checks fail — is data quarantined, does the pipeline halt, is an alert routed to the owning team? Engineers who only mention "we validate in SQL" signal they haven't built quality into the pipeline architecture.

What big data tools and frameworks have you worked with hands-on, and how did you decide which to use for a given problem?

Look for genuine hands-on experience beyond the resume — not just that they've used Spark, but that they've tuned shuffle partitions, managed executor memory, and debugged skew. Strong candidates describe tool selection as a function of data volume, latency requirements, team familiarity, and cost — not just recency or popularity. Engineers who list every major framework without being able to articulate when they'd choose one over another signal resume-padding.

How do you approach optimizing data storage and retrieval for performance in a large-scale warehouse or lake environment?

Strong candidates describe partitioning and clustering strategies, file format choices (Parquet vs. Delta vs. Iceberg), query plan analysis, materialized views, and caching layers. Look for awareness that optimization is workload-specific — the right partition key for a reporting query is different from the right key for a streaming join. Engineers who only mention "add an index" signal they're thinking in OLTP terms.

How do you handle late-arriving data and out-of-order events in a streaming pipeline?

This separates batch-oriented engineers from those with real streaming experience. Look for understanding of watermarks, event time vs. processing time, windowing strategies (tumbling, sliding, session), and the business decision of how long to wait for late data before closing a window. Engineers who haven't worked with streaming often describe a batch workaround rather than a streaming solution — which is a valid answer but signals limited scope.

How do you collaborate with data scientists, analysts, and platform teams to ensure the data you build is actually usable downstream?

The best data engineers treat downstream users as their customers. Look for practices like data contracts, published documentation in a data catalog, SLA agreements on pipeline freshness, and feedback loops with analysts when schema changes break dashboards. Engineers who build pipelines in isolation and hand off a table name are creating technical debt that their downstream colleagues will absorb silently until something breaks in production.