Top 6 Data engineering Interview Questions
Browse 6 real-world technical and behavioral interview questions about Data engineering. Review scenarios, edge cases, and architectural best practices.
How would you design a data lakehouse to handle petabytes of data with frequent GDPR right-to-be-forgotten requests and rapid schema evolution without sacrificing query latency?
An authoritative discussion on Apache Iceberg's table format, focusing on hidden partitioning, the trade-offs of copy-on-write versus merge-on-read, and managing schema drift at scale. Use this data engineering answer to show the decision, trade-off, and evidence rather than a memorised definition. It also connects data lakes to the point an interviewer is testing.
How do you implement dynamic PII masking across a highly decentralised data mesh without destroying the analytical utility of the data for downstream machine learning workloads?
An examination of decentralised data governance, exploring dynamic masking in the compute layer, deterministic tokenisation for referential integrity, and standardised tagging. Use this data engineering answer to show the decision, trade-off, and evidence rather than a memorised definition. It also connects data governance to the point an interviewer is testing.
How do you achieve true exactly-once semantics in Flink across source, state, and sink without cratering throughput?
An analysis of the architectural reality of exactly-once guarantees in Flink, from distributed snapshots to two-phase commits. Use this data engineering answer to show the decision, trade-off, and evidence rather than a memorised definition. It also connects streaming to the point an interviewer is testing.
How do you diagnose and eliminate extreme data skew in a massive Apache Spark ETL pipeline when standard Adaptive Query Execution (AQE) fails to prevent out-of-memory crashes?
An analysis of distributed processing bottlenecks, addressing the limitations of AQE, implementing manual key salting for shuffle optimization, and restructuring data layouts. Use this data engineering answer to show the decision, trade-off, and evidence rather than a memorised definition. It also connects batch processing to the point an interviewer is testing.
How do you architect a strictly low-latency, real-time pipeline to ingest, embed, and index tens of thousands of unstructured documents per second into a vector database?
Designing a robust streaming architecture for embedding generation, detailing GPU adaptive batching, Kafka-driven backpressure management, and asynchronous HNSW indexing. Use this machine learning answer to show the decision, trade-off, and evidence rather than a memorised definition. It also connects data engineering to the point an interviewer is testing.
How do you safely rebuild a corrupted read model in an event-sourced architecture without incurring significant downtime?
Rebuild a corrupted CQRS read model by replaying the event store into a fresh projection, applying upcasters for old event versions, validating parity, and cutting over with a blue-green deployment.