Show that you built data quality checks into the pipeline, not as an afterthought. Describe what happens when a check fails: does the pipeline halt, alert, or load with a warning?
Strong answers cover: source data extraction, transformation logic, data validation checks at each stage, error handling and alerting, idempotent loads for safe reruns, logging, and monitoring. Best candidates discuss the trade-offs between batch and streaming, and how they ensured downstream consumers could trust the data.
Tests data engineering awareness. Not all analysts build pipelines, but understanding them is important. Candidates who can discuss pipeline reliability and failure handling show operational maturity.