**Situation**: Daily ETL processing 50M+ rows took 6+ hours, blocking downstream reports and risking SLA. **Task**: Reduce runtime to under 1 hour while maintaining data quality. **Action**: (1) Profiled with Spark UI and query plans; identified full-table scans and unnecessary...
This hard-level SQL question appears frequently in data engineering interviews at companies like Goldman Sachs. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (etl, join, optimization) will help you answer variations of this question confidently.
This is a senior-level question that tests architectural thinking. Lead with the high-level design, then drill into specifics. Discuss trade-offs explicitly - there is rarely one correct answer. Show awareness of scale, fault tolerance, and operational complexity.
Situation: Daily ETL processing 50M+ rows took 6+ hours, blocking downstream reports and risking SLA. Task: Reduce runtime to under 1 hour while maintaining data quality. Action: (1) Profiled with Spark UI and query plans; identified full-table scans and unnecessary shuffles. (2) Partitioned source by date; enabled partition pruning. (3) Replaced cross-joins with broadcast joins for small dimensions. (4) Implemented incremental processing using watermark columns. (5) Tuned parallelism and executor memory. (6) Added monitoring for regression. Result: Runtime reduced to ~45 minutes; same-day reporting enabled. Documented changes; established optimization playbook for team. Leadership: Drove cross-team alignment on SLA; mentored junior engineers on profiling techniques.
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Analyze My Answer — FreeAccording to DataEngPrep.tech, this is one of the most frequently asked SQL interview questions, reported at 1 company. DataEngPrep.tech maintains a curated database of 1,863+ real data engineering interview questions across 7 categories, verified by industry professionals.