Interview questions
Preparing for a data engineering interview at Amazon? This page contains 22 real interview questions sourced from verified Amazon interview experiences. Questions are sorted by frequency — the ones asked most often appear first.
Amazon data engineering interviews typically focus on SQL, System Design/Architecture, and Behavioral. The interview bar skews toward harder problems (9 hard vs. 8 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.
How have you mentored others in your team or improved team-wide engineering practices?
Tell me about a time you had to work with incomplete or dirty data. How did you manage it?
What motivates you to work on data infrastructure problems?
How would you handle security and privacy concerns when working with sensitive data in a cloud environment?
How do you keep up with the latest trends or tools in data engineering?
Given a list of integers, write a Python function to return the number of unique pairs that sum up to a target.
Describe a scenario where you disagreed with a product or business team. What did you do?
Describe a scenario where you had to make trade-offs between data processing speed and accuracy. How did you approach this situation and what was the outcome?
Describe a situation where you made a mistake in a data pipeline. How did you identify and fix it?
Explain a project where you had to influence stakeholders without having authority.
Explain the process you would follow for optimizing a database query that is running slow.
How would you identify duplicate records based on a composite key in SQL?
In Python, process a large CSV in chunks and remove duplicate records based on email and timestamp.
What strategies and technologies would you consider when designing a data warehouse architecture for efficient data storage and retrieval?
Write a SQL query to detect customers who have not placed a second order in 90 days.
Write a SQL query to find the top 3 selling products per region in the last month.
How would you design a scalable and fault-tolerant data processing pipeline for handling large volumes of streaming data?
Share your experience in working with big data technologies such as Hadoop, Spark, or AWS EMR. How have you leveraged these tools in your previous projects?
Design a data model for an e-commerce system tracking orders, shipments, and payments.
Discuss your experience with ETL (Extract, Transform, Load) processes. What tools and techniques have you used to ensure efficient data extraction and transformation?
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