Amazon Data Engineer Interview Questions

I would treat data quality as a production requirement, not a nice-to-have. That means schema checks, row count validation, freshness checks, and business-rule validation at key stages of the pipeline. The goal is to detect bad data early so downstream consumers do not make decisions on broken inputs.

Idempotency means rerunning the same operation produces the same final result. It matters because retries, reprocessing, and backfills are normal in production systems, and non-idempotent logic can create duplicate or inconsistent results. A strong pipeline design assumes failures will happen and makes reruns safe.

I would inspect the physical plan, stage metrics, shuffle volume, and task durations to see where time is being spent. A data growth spike often exposes skew, memory pressure, or a bad join strategy. I would then tune the partitioning, reduce the amount of data shuffled, or change the execution plan so the job scales better.

I would first confirm skew with task-level metrics, then decide whether to broadcast the small table, salt the hot keys, or pre-aggregate before the join. Skew is dangerous because a few partitions can dominate runtime while the rest finish quickly. The right fix usually reduces imbalance rather than just adding more compute.

I would isolate each source into a consistent landing zone, validate the data before transformation, and capture source metadata for traceability. Since upstream systems fail in different ways, I would also keep each source independently observable and recoverable. The pipeline should be designed so one bad input does not break the entire flow.