1. What is a Data Engineer at Amazon Web Services?
As a Data Engineer at Amazon Web Services (AWS), you are the architect behind the data infrastructure that powers the world's most comprehensive cloud platform. This role is not simply about moving data from point A to point B; it is about designing and implementing massive-scale data warehousing solutions that drive critical business decisions for teams like AWS Marketing D:SE (Data: Science, Engineering) and AWS Global Support. You will work with petabytes of data, integrating heterogeneous sources into centralized warehouses (such as the internal "Jarvis" data warehouse) to enable analytics, machine learning modeling, and economic valuation products.
In this position, you operate at the intersection of software engineering and database architecture. You will own the full lifecycle of data—from ingestion and processing to storage and consumption. Whether you are building robust ETL/ELT pipelines using AWS Glue and Redshift, or optimizing complex SQL queries to improve reporting latency, your work directly impacts how AWS acquires customers and measures revenue growth. You will collaborate with data scientists, business analysts, and software engineers to turn raw logs into actionable insights, ensuring that Amazon remains the market leader in cloud computing.
2. Common Interview Questions
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Curated questions for Amazon Web Services from real interviews. Click any question to practice and review the answer.
Design a batch ETL pipeline that detects, imputes, and monitors missing values before loading analytics tables with daily SLA compliance.
Design a batch data pipeline with quality gates, quarantine handling, and monitored reprocessing for 120M finance records per day.
Design Terraform-based infrastructure as code for AWS data pipelines with reusable modules, secure state management, CI/CD, and drift control.
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Sign up freeAlready have an account? Sign in3. Getting Ready for Your Interviews
Preparation for an Amazon Web Services interview requires a shift in mindset. You are not just being tested on your ability to write code; you are being evaluated on your alignment with Amazon’s culture of ownership and customer obsession.
Your interviewers will evaluate you based on four core pillars:
Data Engineering Fundamentals – You must demonstrate deep expertise in SQL, data modeling (dimensional modeling, Star/Snowflake schemas), and ETL architecture. You will be expected to write highly optimized queries and design schemas that can handle massive scale and query concurrency.
Coding and Scripting – While less intense than a Software Development Engineer (SDE) loop, you must be proficient in a scripting language, typically Python or Scala. You will need to solve algorithmic problems that reflect real-world data manipulation tasks, such as parsing files or transforming data structures.
System Design and Architecture – You will face questions about building end-to-end data platforms. You need to know when to use specific AWS services (e.g., Kinesis for streaming vs. Glue for batch, Redshift vs. DynamoDB) and how to design for fault tolerance, scalability, and data quality.
Amazon Leadership Principles (LPs) – This is the most distinct part of the AWS interview. You will be evaluated on how well you embody principles like Customer Obsession, Bias for Action, and Dive Deep. Every answer you give should reflect these values.
4. Interview Process Overview
The interview process for a Data Engineer at Amazon Web Services is rigorous and structured to assess both technical prowess and cultural fit. It typically begins with an Online Assessment (OA) or a recruiter screen, depending on the specific team and level. The OA usually consists of SQL challenges and coding problems. If you pass, you will move to a phone screen, which serves as a gateway to the final onsite loop.
The "Onsite" (often virtual) is a comprehensive loop consisting of 4–5 separate interviews, each lasting about 60 minutes. Unlike many other companies, AWS assigns each interviewer a specific set of Leadership Principles and technical competencies to vet. You will meet with other Data Engineers, a Hiring Manager, and a "Bar Raiser"—an interviewer from a different team whose sole purpose is to ensure you are better than 50% of the current employees in the role. Expect a mix of whiteboard coding, system design on a virtual board, and intense behavioral questioning.
This timeline illustrates the progression from your initial application through the multi-stage evaluation. Use this to plan your study schedule: front-load your SQL and Python practice for the screens, then shift your focus to System Design and Leadership Principle stories ("STAR" method) as you approach the final loop.
5. Deep Dive into Evaluation Areas
The Amazon Web Services interview loop is designed to probe the depth of your knowledge. You cannot simply know how to use a tool; you must understand why it is the right tool for the job.
SQL and Data Modeling
This is the most critical technical area. You will be asked to write complex SQL by hand. Interviewers expect you to understand database internals, not just syntax. Be ready to go over:
- Advanced SQL – Window functions (
RANK,LEAD,LAG), complex joins, and CTEs. - Dimensional Modeling – Designing Star and Snowflake schemas, handling Slowly Changing Dimensions (SCD Type 1 vs. Type 2), and normalization vs. denormalization.
- Performance Tuning – Query optimization, understanding execution plans, distribution keys, and sort keys in Redshift.
- Advanced concepts – Handling skewed data, partitioning strategies, and columnar storage mechanics.
Example questions or scenarios:
- "Design a data model for an e-commerce order system that handles millions of transactions daily."
- "Write a query to find the top 3 revenue-generating products per category for the last rolling 30 days."
- "How would you optimize a query that is performing a hash join on two billion-row tables?"
Big Data System Design
You will be given an abstract business problem and asked to architect a solution using AWS native tools. Be ready to go over:
- ETL Architecture – Batch processing vs. stream processing (Lambda/Kinesis).
- AWS Ecosystem – Deep knowledge of Redshift, Glue, EMR, S3, and Athena.
- Data Quality – How to implement checks, handle bad data, and ensure idempotency in your pipelines.
- Advanced concepts – Designing for "Exabyte scale," handling backfills without downtime, and disaster recovery planning.
Example questions or scenarios:
- "Design a pipeline to ingest clickstream data in real-time and aggregate it for a marketing dashboard."
- "How would you migrate a legacy on-premise data warehouse to Amazon Redshift with minimal downtime?"
Coding and Algorithms
Expect practical scripting questions. You are not usually expected to solve dynamic programming hard problems, but you must write clean, functional code. Be ready to go over:
- Data Structures – Arrays, Dictionaries/Hash Maps, Sets, and Strings.
- File Parsing – Reading a CSV or JSON file and transforming the data.
- Logic – Basic algorithms to manipulate data sets (e.g., deduplication, aggregation).
Example questions or scenarios:
- "Write a Python script to parse a log file and count the occurrence of specific error codes."
- "Given a list of dictionaries representing user sessions, merge overlapping sessions."
