Real questions asked in Amazon data engineering interviews. Covers SQL, system design, AWS services, and behavioral rounds with Leadership Principles.
Amazon's data engineering interviews are rigorous, spanning technical SQL deep-dives, system design for data pipelines at scale (using Redshift, Glue, EMR, S3, Kinesis), Python/Spark coding, and behavioral questions mapped to their 16 Leadership Principles. These questions are sourced from actual Amazon interview loops.
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?
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?
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.
Given a list of integers, write a Python function to return the number of unique pairs that sum up to a target.
How do you keep up with the latest trends or tools in data engineering?
How have you mentored others in your team or improved team-wide engineering practices?
How would you build a pipeline that transforms semi-structured logs into a structured analytics layer?
How would you design a scalable and fault-tolerant data processing pipeline for handling large volumes of streaming data?
How would you ensure data quality and integrity in a data pipeline? Discuss the steps you would take to validate and cleanse data.
How would you handle security and privacy concerns when working with sensitive data in a cloud environment?
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.
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?
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?
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.
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