Interview questions
Preparing for a data engineering interview at Goldman Sachs? This page contains 41 real interview questions sourced from verified Goldman Sachs interview experiences. Questions are sorted by frequency — the ones asked most often appear first.
Goldman Sachs data engineering interviews typically focus on SQL, System Design/Architecture, and General/Other. The interview bar skews toward harder problems (19 hard vs. 7 easy), suggesting emphasis on depth and system-level thinking.
Use the difficulty filters above to focus your preparation. For each question, attempt your own answer first, then compare with our expert solution. You can also practice these questions in our AI Mock Interview Coach for real-time feedback.
Describe a scenario where partitioning and bucketing would improve query performance.
When would you choose a Snowflake schema over a Star schema?
Implement a query to find the top 5 customers by total sales amount.
Write an SQL query to find duplicate emails in a users table.
Given a streaming dataset from Kafka, how would you ingest the data in real-time using Spark?
Tell me about a time you handled a data pipeline failure during a critical operation.
Compare batch processing and stream processing for financial data.
Compute the moving average of daily transactions over a 7-day window.
Describe a time when you had to deal with a major data quality issue. How did you handle it?
Describe the concept of data sharding and when to use it.
Explain how you ensure data security and compliance in sensitive data projects.
How do you prioritize competing demands in a high-pressure environment?
How would you handle data quality issues in a real-time ingestion pipeline?
How would you model hierarchical data in a relational database?
How would you handle memory constraints when processing a large dataset in Python?
How would you process a 10TB dataset on a single machine in Python?
Implement a recursive algorithm to find the nth Fibonacci number.
Write a Python script to parse a large JSON file, filter records based on a condition, and write the result to a database.
Write code to merge two sorted arrays without using extra space.
Compare OLTP and OLAP systems in the context of financial transactions.
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