Preparing for an Amazon interview requires a shift in mindset. You must demonstrate technical excellence, but equally important is your alignment with the company's core values. Do not underestimate the behavioral component; it carries as much weight as your coding skills.

You will be evaluated on the following key criteria:

Data Engineering Fundamentals – You must demonstrate deep knowledge of database internals, data modeling (dimensional modeling, Star/Snowflake schemas), and ETL design patterns. Interviewers will test your ability to write complex, optimized SQL and your understanding of distributed computing concepts.

Coding & Algorithms – While not as intense as a Software Development Engineer (SDE) interview, you are expected to write clean, production-quality code in Python, Scala, or Java. You will be tested on data structures (arrays, dictionaries, sets) and algorithms suitable for data manipulation.

Amazon Leadership Principles (LPs) – This is the most distinct part of the Amazon culture. You will be evaluated on how you have demonstrated principles like "Dive Deep," "Bias for Action," and "Deliver Results" in your past work. You must prepare stories using the STAR method (Situation, Task, Action, Result) that map directly to these principles.

System Design – For mid-to-senior roles, you will face questions on designing scalable data architectures. You need to know when to use a relational database versus a NoSQL store, how to handle data quality checks, and how to architect for fault tolerance on AWS.

The interview process for a Data Engineer at Amazon is rigorous, structured, and designed to eliminate false positives. Based on recent candidate experiences, the process typically moves from an online assessment to a phone screen, culminating in a comprehensive onsite "Loop." The timeline can vary, but Amazon generally moves quickly once you pass the initial screens.

You should expect the process to start with an Online Assessment (OA) focusing on SQL and basic coding, often hosted on platforms like HackerRank. If successful, you will proceed to a technical phone screen (or video call) involving live coding and a deep dive into your resume. The final stage is the "Loop"—a series of 4–5 back-to-back interviews (virtual or in-person) where you will meet with Data Engineers, Managers, and a "Bar Raiser." The Bar Raiser is a designated interviewer from a different team whose sole job is to ensure you are better than 50% of the current employees in the role.

Throughout this process, consistency is key. You might answer a technical question perfectly, but if you fail to demonstrate the Leadership Principles or show a lack of "Customer Obsession," you will likely not receive an offer. The difficulty is generally rated as Medium to Difficult, with a heavy emphasis on your ability to explain why you made specific technical decisions.

This timeline illustrates the standard progression from your application to the final offer. Use the time between the phone screen and the onsite Loop to heavily practice your STAR stories for behavioral questions, as this is where many technically strong candidates fail. Be prepared for a marathon day during the final stage; managing your energy and maintaining enthusiasm through 4+ hours of interviews is essential.

The following areas represent the core pillars of the Amazon Data Engineer interview. You should allocate your study time based on these categories, noting that SQL and Leadership Principles are often the primary filters.

SQL and Data Modeling

This is the bread and butter of the role. You will not just be asked to write queries; you will be asked to optimize them. Interviewers expect you to handle complex aggregations, window functions, and schema designs on the fly.

Be ready to go over:

• Complex SQL Queries – Joins (Inner, Left, Cross), Window Functions (RANK, LEAD, LAG), and CTEs.
• Data Modeling – Designing Star and Snowflake schemas, handling Slowly Changing Dimensions (SCD Type 1, 2, 3).
• Performance Tuning – Understanding execution plans, indexing strategies, and partitioning.
• Advanced concepts – Skew handling in distributed joins and query optimization in columnar databases (like Redshift).

Example questions or scenarios:

• "Write a query to find the top 3 selling products per category for the last month."
• "How would you design a schema to track historical changes in customer addresses?"
• "Debug a query that is running slowly on a billion-row table."

Coding and Algorithms

Amazon expects Data Engineers to be proficient coders. The focus is usually on data manipulation rather than complex graph traversal or dynamic programming, but you must write syntactically correct code.

Be ready to go over:

• Data Structures – Arrays, Hash Maps (Dictionaries), Sets, and Strings.
• Algorithms – Sorting, searching, and two-pointer techniques.
• Scripting – File parsing, string manipulation, and API interaction using Python.

Example questions or scenarios:

• "Given an array of integers, return indices of the two numbers such that they add up to a specific target (Two Sum)."
• "Find the second largest element in an array without sorting."
• "Validate if a string has properly closed parentheses."

Big Data Frameworks & System Design

You need to demonstrate that you can work outside of a single-node database. Questions here focus on the "Engineer" part of the title—building systems that don't crash under load.

Be ready to go over:

• Distributed Processing – Spark (PySpark) fundamentals, RDDs vs. DataFrames, and lazy evaluation.
• AWS Ecosystem – Redshift, S3, EMR, Glue, and Lambda.
• Pipeline Design – Batch vs. Streaming (Kinesis/Kafka), idempotency, and backfilling data.

Example questions or scenarios:

• "Explain how you would migrate an on-premise data warehouse to AWS."
• "How do you handle data skew in a Spark join operation?"
• "Design a pipeline to ingest clickstream data in real-time."

Leadership Principles (Behavioral)

This is not a "soft skill" check; it is a rigorous evaluation. You will be asked "Tell me about a time..." questions for nearly every principle.

Be ready to go over:

• Customer Obsession – Prioritizing customer needs over technical perfection.
• Ownership – Going beyond your job description to fix a problem.
• Bias for Action – Making a calculated decision with incomplete data.
• Have Backbone; Disagree and Commit – Respectfully challenging a decision when you disagree.

As a Data Engineer at Amazon, your day-to-day work revolves around enabling data availability and quality. You are the builder who connects raw data sources to analytical endpoints.

You will spend a significant portion of your time designing and maintaining ETL pipelines. This involves writing code to extract data from various internal services, transforming it to meet business logic requirements, and loading it into data lakes (S3) or data warehouses (Redshift). You will frequently collaborate with Software Development Engineers to understand upstream data formats and with Business Intelligence Engineers to understand downstream reporting needs.

Beyond pipeline construction, you are responsible for infrastructure health. You will monitor data quality, troubleshoot pipeline failures, and optimize query performance to ensure SLAs are met. In many teams, you will also be involved in architectural discussions, helping to decide which AWS services to leverage for new initiatives. You are expected to automate manual processes and constantly look for ways to reduce technical debt.

To succeed in this interview and role, you need a specific mix of technical hard skills and adaptive soft skills.

• Technical Skills (Must-Have) – Proficiency in SQL is non-negotiable; you must be able to write advanced queries from scratch. You need strong coding skills in Python or Java. Experience with dimensional data modeling and schema design is essential.
• Technical Skills (Nice-to-Have) – Experience with AWS services (Redshift, Glue, EMR, Athena) is a massive plus. Familiarity with Spark/PySpark and big data frameworks will set you apart. Knowledge of infrastructure as code (Terraform, CloudFormation) is increasingly valuable.
• Experience Level – Typically, candidates have a background in Computer Science or a related field, with prior experience in BI or Data Engineering roles. For L4/L5 roles, interviewers look for evidence of end-to-end project ownership.
• Soft Skills – You must be able to communicate complex technical concepts to non-technical stakeholders. The ability to navigate ambiguity—figuring out what to build when requirements are vague—is critical at Amazon.