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.
This collection contains 12 curated questions: 2 easy, 1 medium, and 9 hard. The distribution skews toward harder problems, reflecting the depth expected in senior-level interviews.
The most frequently tested areas in this set are partition (8), spark (7), optimization (5), join (5), window (3), and python (2). Focusing on these topics will give you the highest return on your preparation time.
Start with the easy questions to warm up and solidify fundamentals. Medium-difficulty questions form the bulk of real interviews — spend the most time here and practice explaining your reasoning out loud. Hard questions often appear in senior and staff-level rounds; attempt them after you're comfortable with the basics. For each question, try answering before revealing the solution. Use our AI Mock Interview to simulate real interview conditions and get instant feedback on your responses.
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?
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 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?
What strategies and technologies would you consider when designing a data warehouse architecture for efficient data storage and retrieval?
Typically a recruiter screen, an online assessment or phone screen with SQL and coding, then a full onsite loop of 4–5 rounds: SQL deep-dive, data modeling/system design for pipelines, a Python/Spark coding round, and one or more behavioral rounds. Every round, including technical ones, weaves in Leadership Principles, and a Bar Raiser sits in to keep the bar consistent.
Expect Redshift (distribution and sort keys, performance tuning), S3 as the data lake, Glue for ETL and the Glue Data Catalog, EMR for Spark, Kinesis for streaming, Athena for ad-hoc querying, and sometimes Lambda and Step Functions for orchestration. You should be able to design an end-to-end pipeline choosing the right service for each stage and justify the trade-offs.
Yes — Leadership Principles are central. Behavioral answers should use the STAR format (Situation, Task, Action, Result) with concrete, metric-backed stories mapped to principles like Customer Obsession, Ownership, Dive Deep, and Deliver Results. Even technical rounds expect you to demonstrate ownership and bias for action while you reason.
Very. SQL is the most heavily weighted technical skill — expect complex joins, window functions, deduplication, and analytical queries under time pressure, often against realistic schemas. Most rejections happen in the SQL and data-modeling rounds, so prioritize them in your prep.
Practice dimensional modeling (star/snowflake schemas), slowly changing dimensions (SCD Type 2), fact vs dimension grain, and designing schemas from ambiguous requirements. Be ready to defend choices around normalization, partitioning, and incremental loads, and to extend your model when the interviewer adds new requirements mid-round.
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