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Data Infrastructure and Architecture Experience Questions

A prompt to describe the candidate's hands on experience building and operating data infrastructure. Candidates should be prepared to discuss specific pipelines, ETL or ELT systems, streaming frameworks, data warehouses and lakes, the scale of data processed, tooling and platforms used, performance and cost trade offs they made, monitoring and data quality practices, incidents or scalability challenges they addressed, and measurable outcomes or improvements resulting from their work.

MediumTechnical
53 practiced
Kafka scaling problem: Consumers are consistently lagging and partition count is at the maximum for current topics. Walk through options to improve throughput and reduce lag (e.g., re-partitioning, consumer parallelism, batching, compression), with pros and cons and operational considerations.
HardTechnical
61 practiced
Security incident: A production ETL join error caused PII (masked SSNs) to be written to an internal analytics dashboard accessible to more employees than intended. Walk through the steps to detect, contain, remediate, communicate, and prevent recurrence including technical fixes, audits, and process changes.
MediumTechnical
55 practiced
SQL challenge: Using the events table (user_id, event_ts TIMESTAMP, event_name), write a query to compute Daily Active Users (DAU) and the 7-day rolling retention rate for each day. Specify how you would handle timezone normalization and late-arriving events in the query or the pipeline.
EasyTechnical
61 practiced
Explain the core differences between batch processing and streaming processing for analytics. Include latency, throughput, complexity, state management, fault tolerance, and typical tooling. Give examples from projects where you picked batch over streaming and vice versa and why.
EasyBehavioral
73 practiced
Behavioral: Tell me about a time you improved the reliability of a production data pipeline. Describe the situation, the root cause you identified, the technical changes you made (e.g., retries, idempotency, isolation), how you validated the fix, and what measurable improvements followed (SLA, failure rate, MTTR).

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