Goldman Sachs Data Engineer Interview Questions & Answers (2026)
Practice the 41 most asked data engineering questions at Goldman Sachs. Covers SQL, Spark/Big Data, Behavioral and more.
Key Takeaways
- ✓Why Goldman Sachs Tests These Questions
- ✓Top 5 Most Asked Questions at Goldman Sachs
- ✓Category Breakdown for Goldman Sachs Interviews
- ✓How to Prepare
Why Goldman Sachs Tests These Questions
Goldman Sachs is known for rigorous data engineering interviews that focus on practical, production-level knowledge. With 41 questions in our vault, the most common category is SQL (16 questions).
Difficulty breakdown: 7 easy, 15 medium, 19 hard. Expect system design and optimization questions at senior levels.
Top 5 Most Asked Questions at Goldman Sachs
- Q1: Describe a scenario where partitioning and bucketing would improve query performance.
- Q2: When would you choose a Snowflake schema over a Star schema?
- Q3: Implement a query to find the top 5 customers by total sales amount.
- Q4: Write an SQL query to find duplicate emails in a users table.
- Q5: Given a streaming dataset from Kafka, how would you ingest the data in real-time using Spark?
Category Breakdown for Goldman Sachs Interviews
- SQL: 16 questions
- System Design/Architecture: 10 questions
- General/Other: 7 questions
- Python/Coding: 5 questions
- Spark/Big Data: 2 questions
- Behavioral: 1 questions
How to Prepare
Focus on SQL questions first, as they dominate Goldman Sachs's interview pattern. Practice the top-frequency questions below, then move to adjacent categories. For senior roles, expect 1-2 system design rounds.
Reviewed by Aditya Kumar · DataEngPrep Editorial Team
Drafted by the editorial team and signed off by Aditya Kumar, founder and lead editor at DataEngPrep. Questions are sourced from real interviews, initial answers are drafted with AI assistance, and every section is human-edited for technical accuracy, relevance to current FAANG hiring rubrics, and clarity. Articles are reviewed periodically as interview patterns evolve.
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