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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.
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.
Practice These Questions
mediumDescribe a scenario where partitioning and bucketing would improve query performance.→mediumWhen would you choose a Snowflake schema over a Star schema?→mediumImplement a query to find the top 5 customers by total sales amount.→mediumWrite an SQL query to find duplicate emails in a users table.→hardGiven a streaming dataset from Kafka, how would you ingest the data in real-time using Spark?→
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