Interview questions
Preparing for a data engineering interview at Amazon? This page contains 11 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 System Design/Architecture, SQL, and Spark/Big Data. The interview bar skews toward harder problems (9 hard vs. 1 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 would you handle security and privacy concerns when working with sensitive data in a cloud environment?
Given a list of integers, write a Python function to return the number of unique pairs that sum up to a target.
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?
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?
How would you build a pipeline that transforms semi-structured logs into a structured analytics layer?
How would you ensure data quality and integrity in a data pipeline? Discuss the steps you would take to validate and cleanse data.
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