Screen data engineers on pipeline design, modeling, orchestration, data quality, and production-grade analytics infrastructure.
Strong data engineers are operators, not just SQL writers. These prompts test pipeline reliability, schema evolution, idempotency, and how candidates think about data quality at scale.
Test orchestration, idempotency, and recovery.
Discusses idempotent writes, dedupe keys, backfills, schema registry, and monitoring.
Evaluate dimensional modeling, slowly changing dimensions, and analytics access patterns.
Discusses query patterns, governance, BI tool behavior, and update cost.
Check whether candidates treat data quality as a first-class concern.
Reasons across ingestion, transformation, modeling, and consumption layers; uses lineage and tests.
Put a candidate through a structured AI or human-led round and get a reportcard with the transcript and the code attached.