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. 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.

3. 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.

4. 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."

5. Key Responsibilities

As a Data Engineer, your daily work revolves around enabling data-driven decisions for teams like Marketing or Infrastructure Services. You are responsible for the "plumbing" that keeps the business running.

• Pipeline Development: You will design, build, and maintain robust ETL/ELT pipelines using SQL and Python. This often involves orchestrating jobs in AWS Glue or Data Pipeline to move data from production services into the data warehouse.
• Data Warehousing: You will manage and optimize large-scale data warehouses, primarily on Amazon Redshift. This includes defining schemas, managing access controls, and tuning performance to ensure reports load quickly for business users.
• Cross-Functional Collaboration: You will interface directly with business owners, data scientists, and software engineers to gather requirements. You must translate vague business questions (e.g., "How is our marketing campaign performing?") into concrete technical specifications and data sets.
• Data Quality & Operations: You are an owner. This means you are responsible for the operational health of your data. You will set up monitoring, alert on pipeline failures, and perform root cause analysis when data discrepancies occur.

6. Role Requirements & Qualifications

Candidates for this role are expected to be hands-on builders with a solid theoretical foundation in database engineering.

• Must-have skills:

  • Expert-level SQL: Ability to write and optimize complex queries is non-negotiable.
  • Data Modeling: Strong grasp of Kimball dimensional modeling techniques.
  • Programming: Proficiency in Python (preferred) or Scala/Java for scripting and ETL tasks.
  • Big Data Technologies: Experience with MPP databases (like Redshift, Teradata, or Snowflake) and big data frameworks (Spark, Hadoop).
  • AWS Cloud Experience: Familiarity with services like S3, EC2, Lambda, and Glue.

• Nice-to-have skills:

  • Experience with Infrastructure as Code (IaC) tools like CloudFormation or Terraform.
  • Knowledge of BI tools like Amazon QuickSight or Tableau.
  • Experience in a DevOps environment, using CI/CD pipelines for data infrastructure.
  • Background in software development or technical support (as seen in Database Engineer profiles).

7. Common Interview Questions

The following questions are representative of what candidates face in AWS Data Engineer loops. They are designed to test your technical skills in the context of the Leadership Principles. Do not memorize answers; instead, understand the underlying concepts.

Technical & SQL

• "Write a query to find the second highest salary in each department. If there is a tie, how do you handle it?"
• "Explain the difference between a LEFT JOIN and an INNER JOIN. When would a CROSS JOIN be useful?"
• "How would you design a schema to track customer support tickets and their escalation history?"
• "What is the difference between a Star Schema and a Snowflake Schema? Why would you choose one over the other in Redshift?"
• "How do you handle duplicate data arriving in your S3 bucket before loading it into the warehouse?"

Behavioral (Leadership Principles)

• Customer Obsession: "Tell me about a time you had to compromise on a technical requirement to meet a customer need."
• Dive Deep: "Describe a time when you debugged a complex data issue where the root cause was not immediately obvious."
• Bias for Action: "Tell me about a time you had to make a decision with incomplete data. What was the outcome?"
• Deliver Results: "Give an example of a time you significantly improved the performance of a data pipeline or query."

System Design

• "Design a system to calculate the top trending products on Amazon.com in real-time."
• "How would you architect a data lake solution for a company that generates 5TB of logs per day?"
• "We have a legacy SQL Server database that needs to be migrated to the cloud. Walk me through your migration strategy."

8. Frequently Asked Questions

Q: How difficult is the coding portion compared to an SDE interview? The coding bar for Data Engineers is generally lower than for Software Development Engineers. You will likely not face complex dynamic programming or graph traversal problems. Focus on array manipulation, string parsing, and dictionary/hash map logic. The emphasis is on writing clean, maintainable code that solves data problems.

Q: What is the "Bar Raiser"? The Bar Raiser is a unique Amazon interviewer from a different organization who holds veto power over the hiring decision. Their job is to ensure you raise the performance bar of the team. They focus heavily on Leadership Principles. You will not know who the Bar Raiser is during the loop, so treat every interviewer with equal importance.

Q: Do I need to know AWS services specifically? While general big data knowledge is acceptable, knowing the AWS equivalents (e.g., Redshift for warehousing, Kinesis for streaming, Glue for ETL) is highly advantageous. If you know Spark but not Glue, explain your solution in Spark and mention you would map it to Glue in the AWS ecosystem.

Q: How much should I prepare for the Leadership Principles? Do not underestimate this. Roughly 50% of your evaluation is based on LPs. You should prepare 10–15 stories using the STAR method (Situation, Task, Action, Result) that can be adapted to different principles.

9. Other General Tips

Master the STAR Method: Every behavioral answer must follow the Situation, Task, Action, Result format. Amazon interviewers are trained to drill down into the "Action" and "Result." Be specific about your contribution. Use "I" instead of "We."

Clarify Ambiguity: In system design and SQL questions, the prompt will often be vague on purpose. It is your job to ask clarifying questions about data volume, latency requirements, and edge cases before you start solving. This demonstrates the Are Right, A Lot principle.

Think at Scale: AWS operates at a scale few companies can match. When designing a system, always ask yourself: "Will this break if the data volume triples overnight?" If the answer is yes, your design needs work.

Know Your Database Internals: Don't just write a query; explain how the database engine executes it. Discussing distribution styles (Key vs. Even vs. All) in Redshift or how columnar storage impacts compression can set you apart as a senior candidate.

10. Summary & Next Steps

Becoming a Data Engineer at Amazon Web Services is a career-defining opportunity. You will work on the bleeding edge of cloud technology, solving problems that strictly deal with massive scale and complexity. The role demands a unique blend of technical excellence in SQL and distributed systems, combined with a relentless focus on customer value.

To succeed, structure your preparation around the three core areas: SQL/Data Modeling, System Design, and Leadership Principles. Practice writing SQL on a whiteboard or plain text editor to get comfortable without an IDE. diverse your behavioral stories to show you are a leader who can deliver results under pressure.

The compensation for this role is highly competitive, typically consisting of a base salary, a sign-on bonus (prorated over two years), and Restricted Stock Units (RSUs) that vest over time. This structure is designed to reward long-term impact and ownership.

Prepare thoroughly, dive deep into the details, and approach the interview with the confidence of an owner. Good luck.