Amazon Data Engineer Entry Level Interview Preparation Guide
Amazon's Data Engineer interview process for entry-level candidates consists of 6 distinct stages designed to assess technical proficiency, problem-solving ability, alignment with Amazon's Leadership Principles, and cultural fit. The process begins with a recruiter screening call, progresses through a technical phone screen, and culminates in a comprehensive onsite interview loop with multiple technical rounds, a Bar Raiser evaluation, and an HR/Manager round.[5]
Interview Rounds
Recruiter Screening
What to Expect
The initial recruiter screening call typically lasts 30-45 minutes and serves as the first filter in the hiring process. The recruiter will assess your professional background, understand your motivation for pursuing data engineering, evaluate your communication skills, and gauge cultural fit. They'll explain the Data Engineer role, discuss the team and projects, answer your questions about Amazon, and determine whether you should move forward to technical interviews. This is also an opportunity to express your enthusiasm for data engineering and demonstrate that you've researched Amazon and the position.[1][4]
Tips & Advice
Be authentic and genuinely enthusiastic about data engineering and Amazon. Prepare a clear 1-2 minute introduction focusing on your interest in the field and why you're pursuing this role. Research Amazon's business, key products (AWS, retail, Prime Video, etc.), and mention specific areas that interest you. For entry-level candidates, it's completely acceptable to mention that you're early in your career—frame this as eagerness to grow rather than a limitation. Ask thoughtful questions about the team, role responsibilities, growth opportunities, and technical challenges. Show respect for Amazon's business and culture. Avoid generic answers; demonstrate that you specifically want to work at Amazon, not just any tech company.
Focus Topics
Communication Skills and Professional Presence
Practice speaking clearly at a measured pace without rambling. Keep answers concise and focused on the key points. Show enthusiasm through your tone and word choice. For entry-level candidates, it's fine and expected to mention areas where you're still learning—frame these as growth opportunities and areas you're excited to develop. Demonstrate respect for the interviewer and professionalism in your demeanor.[1][4]
Practice Interview
Study Questions
Role-Specific Interest and Amazon Product Knowledge
Demonstrate understanding of what Data Engineers do at Amazon: building and maintaining infrastructure for data collection, storage, and processing at scale; creating data pipelines; developing ETL processes; ensuring data quality. Show you've researched Amazon's business and mention specific AWS services (S3, Redshift, Glue, EMR) or Amazon services you're interested in working on. For entry-level, basic knowledge is sufficient; the goal is to show genuine interest in the specific role and company.[1][2]
Practice Interview
Study Questions
Amazon Leadership Principles Overview
Familiarize yourself with Amazon's 16 Leadership Principles including Customer Obsession, Ownership, Invent and Simplify, Deliver Results, Learn and Be Curious, and others. For this recruiter call, you don't need to tell deep stories yet, but demonstrate awareness of these values through your language and approach. Show that you understand Amazon's culture is defined by these principles and that you're interested in working in this environment.[2]
Practice Interview
Study Questions
Professional Background and Career Motivation
Articulate your background clearly and concisely. For entry-level candidates, this might include your academic background in computer science or related fields, relevant academic projects, personal data engineering projects, online courses (such as Udemy, Coursera), or internships. Clearly explain why data engineering interests you specifically. Connect your motivation to the role at Amazon, such as interest in building data systems at scale, working with large datasets, or contributing to Amazon's core data infrastructure.[2]
Practice Interview
Study Questions
Technical Phone Screen
What to Expect
The technical phone screen is a critical 60-minute conversation with a Data Engineer or technical interviewer assessing your core technical abilities. You'll encounter 2-3 SQL questions and 1 data modeling scenario. The interviewer is evaluating your SQL proficiency, fundamental understanding of data engineering concepts (ETL, data pipelines, data modeling), logical thinking, and problem-solving approach. This round serves as a filtering stage; strong SQL fundamentals are essential to advance to onsite interviews.[1][2][4]
Tips & Advice
SQL is fundamental and non-negotiable; master it before this round. For each SQL question, first clarify requirements by asking questions: What data? What's the output format? Are there edge cases? Then explain your approach out loud before writing code. Write clean, readable SQL using proper formatting. If you get stuck, explain what you've tried and your thought process rather than going silent—interviewers value seeing your problem-solving approach. For data modeling, ask clarifying questions about business requirements before designing schemas. Time management is critical; if you can't complete a question, solve as much as you can and move on. Entry-level candidates aren't expected to be perfect; demonstrate solid fundamentals and good problem-solving methodology.[1][2][4]
Focus Topics
ETL Concepts and Data Pipeline Fundamentals
Understand the complete ETL flow: Extract (sourcing data from various systems), Transform (cleaning, validating, enriching, aggregating data), and Load (moving processed data to target systems like data warehouses). Understand that ETL is essential for moving raw data from operational systems into analytical systems. Know the difference between batch ETL (processing data in scheduled chunks) and streaming ETL (processing continuous data). Understand data quality considerations: handling nulls, duplicates, and invalid data. For entry-level, you should be able to discuss these concepts clearly and explain how they fit together in a data pipeline.[1][2]
Practice Interview
Study Questions
Problem Analysis and Clear Communication
Develop the habit of thinking out loud and explaining your approach as you work through problems. When given a SQL or data modeling problem, clarify what's being asked by asking questions. Discuss your assumptions and reasoning before writing code or designing schemas. Walk the interviewer through your thought process step-by-step. If you get stuck or reach a dead end, verbalize what you've tried and what you're thinking next—this helps the interviewer understand your problem-solving approach. For entry-level candidates, showing good process and reasoning matters significantly, even if your solution isn't perfect.[1][4]
Practice Interview
Study Questions
Data Modeling Basics and Schema Design
Understand how to design simple database schemas for business problems. Learn the difference between fact and dimension tables in a star schema. Understand normalization principles and when to denormalize for performance. Practice designing schemas for simple business domains: e-commerce systems with users and orders, activity tracking systems with events and users, or payment systems. Ask clarifying questions before designing: What entities exist? What are the relationships? What queries will be run? What's the data volume? For entry-level, you're not expected to handle complex enterprise data models, but you should understand fundamental design principles and be able to explain your decisions.[2][4]
Practice Interview
Study Questions
SQL Fundamentals: JOINs, Aggregations, and Subqueries
Master core SQL skills tested in every interview: INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL OUTER JOIN, and CROSS JOIN. Understand GROUP BY and HAVING clauses for aggregations. Practice writing subqueries and correlated subqueries. Know when each join type is appropriate for different scenarios. Practice common patterns like finding the second highest salary, counting occurrences, or aggregating across groups. These fundamentals are the building blocks for every SQL question.[1][2]
Practice Interview
Study Questions
Onsite Interview - Technical Round 1
What to Expect
This 45-60 minute technical interview digs deeper into SQL and data modeling than the phone screen. You'll encounter 1-2 complex SQL problems involving multiple joins, aggregations, window functions, or CTEs, and a data modeling scenario where you'll design a schema for a real-world business problem. The interviewer assesses your ability to write clean, correct SQL; optimize queries for readability and performance; design appropriate data models; and communicate your reasoning throughout. This round evaluates technical depth and your ability to think systematically about data structure and access patterns.[1][3][4]
Tips & Advice
Write SQL carefully and think through your approach before coding. Start with the simplest correct solution, then optimize if time permits. For entry-level, correctness is more important than optimization. Use CTEs (WITH clauses) to break down complex queries into readable, logical parts. For data modeling, discuss your design choices: Why this schema? What queries will it support? How does it handle this scenario? Explain your assumptions and reasoning. For entry-level candidates, simple but correct solutions are preferable to overly complex approaches. If you reach a point where you're uncertain, ask for clarification rather than making wrong assumptions. Show awareness of performance considerations even if you don't deeply optimize.[1][3][4]
Focus Topics
SQL Query Optimization and Performance Awareness
Understand basic principles of query optimization: choosing appropriate joins, avoiding SELECT *, using WHERE clauses to filter early, leveraging indexes, and writing efficient subqueries. Learn to think about query performance even if you can't deeply optimize. Understand that for entry-level, correctness is more important than perfect optimization, but showing awareness of performance demonstrates growth mindset. Know when a solution might perform poorly and be able to discuss potential improvements.[1][3][4]
Practice Interview
Study Questions
Data Quality, Validation, and Data Issues
Understand common data quality problems: null values, duplicates, out-of-range values, inconsistent formats, and data type mismatches. Learn to validate data using SQL queries: counting nulls, identifying duplicates, checking value ranges, and detecting inconsistencies. Discuss strategies for handling bad data: filtering it out, imputing missing values, or flagging problematic records. Understand the impact of poor data quality on downstream analysis and business decisions. For entry-level, demonstrate awareness that data quality is crucial and requires deliberate validation and cleaning efforts.[1][3]
Practice Interview
Study Questions
Advanced SQL: Window Functions, CTEs, and Complex Queries
Master window functions for analytical queries: ROW_NUMBER for ranking, RANK/DENSE_RANK for handling ties, LAG/LEAD for accessing previous/next rows, and SUM/AVG as window functions. Understand PARTITION BY and ORDER BY within window functions. Practice Common Table Expressions (CTEs) using WITH clauses to make complex queries readable by breaking them into logical steps. Combine multiple CTEs, window functions, aggregations, and joins in single queries. Practice problems like finding cumulative totals, calculating month-over-month changes, or ranking items within groups. These tools are essential for data analysis queries at scale.[1][3][4]
Practice Interview
Study Questions
Complex Data Modeling for Real-World Scenarios
Design schemas for realistic business problems: e-commerce platforms with products, users, orders, and reviews; payment systems with transactions and reconciliations; recommendation engines with user-item interactions; or user activity tracking systems. For each scenario, discuss your design choices: What are the entities? What relationships exist? Should tables be normalized or denormalized? How will the schema support both data collection and analytical queries? Understand different schema patterns like star schemas (fact + dimensions) and snowflake schemas (further normalized dimensions). Discuss trade-offs in your design.[1][3][4]
Practice Interview
Study Questions
Onsite Interview - Technical Round 2
What to Expect
This 45-60 minute technical interview focuses on ETL, data pipeline design, data warehousing architecture, big data technologies, and AWS data services. You'll discuss how to build end-to-end data pipelines, design data warehouse solutions, handle data at scale, and select appropriate technologies. Questions might involve designing a data pipeline for a specific scenario, explaining how you'd handle data transformations, discussing trade-offs between batch and real-time processing, or explaining when to use specific AWS services. For entry-level candidates, the focus is on understanding core concepts and basic design thinking rather than solving complex distributed systems problems.[1][2][3]
Tips & Advice
When asked to design a data pipeline or data warehouse, structure your thinking: First, understand requirements (what data, where from, what transformations, where stored, who consumes it). Second, design the architecture with clear layers (ingestion, processing, storage, consumption). Third, discuss trade-offs between approaches (batch vs. real-time, different storage options, different technologies). Draw diagrams or use pseudocode to explain your thinking. For entry-level candidates, showing structured thinking about the problem and awareness of trade-offs is more important than knowing the perfect technology choice. Be honest about what you don't know but show enthusiasm to learn. Discuss AWS data services at a basic level; entry-level candidates aren't expected to be AWS experts but should be familiar with major services mentioned in the job description.[1][2][3]
Focus Topics
Big Data Technologies: Apache Spark and Hadoop Fundamentals
Understand the purpose and basic concepts of big data technologies mentioned in the job description: processing data at volumes that exceed single-machine capacity. Know that Spark provides distributed processing with DataFrames and transformations (map, filter, join) and actions (collect, count). Know that Hadoop includes HDFS (Hadoop Distributed File System) for distributed storage and MapReduce for distributed processing. Understand when you'd use these technologies vs. traditional databases. For entry-level, understand these concepts and when to apply them; deep implementation expertise isn't expected but should be a learning goal.[1][2][3]
Practice Interview
Study Questions
AWS Data Tools and Services (S3, Glue, Redshift, EMR, Lambda)
Understand the basic capabilities and use cases for Amazon's data services: S3 for scalable object storage and as the foundation of data lakes, Redshift for data warehousing and analytical queries, Glue for ETL job creation and metadata management, EMR for running distributed computing frameworks like Spark and Hadoop on clusters, and Lambda for serverless compute for lightweight transformations. Know when to use each service and why. For entry-level, basic knowledge of what each tool does and when to use it is sufficient; deep implementation experience isn't expected but should be listed as a learning area.[1][2][3]
Practice Interview
Study Questions
Data Warehousing Architecture and Design
Understand the purpose and structure of data warehouses: centralized repositories of historical data optimized for analysis. Know key concepts: fact tables (measurements/transactions), dimension tables (descriptive attributes), slowly changing dimensions, star schemas (denormalized for query performance), snowflake schemas (further normalized). Discuss designing a data warehouse for a specific business domain from the job description. Explain which tables would be facts vs. dimensions, how they relate, and why this design supports analytical queries. Understand that data warehouses support complex analytical queries on historical data at the expense of fast writes. For entry-level, understand the fundamentals; you're not expected to design enterprise data warehouses.[1][2][3]
Practice Interview
Study Questions
ETL Pipeline Design and Data Transformation at Scale
Design end-to-end ETL pipelines for various business scenarios from the job description: data ingestion from multiple sources, transformation of data (cleaning, enriching, aggregating), and loading into target systems. Discuss extraction from different source types: databases, APIs, files. Discuss transformation logic including handling data quality issues. Understand different orchestration and execution approaches like AWS Glue, Lambda, or Step Functions. Know the trade-offs between batch ETL (processing data on a schedule, simpler, higher latency) and real-time/streaming ETL (processing data continuously, more complex, lower latency). For entry-level, focus on clear system design thinking and ability to discuss these concepts rationally rather than deep implementation details.[1][2][3]
Practice Interview
Study Questions
Onsite Interview - Bar Raiser Round
What to Expect
The Bar Raiser round is a 45-60 minute evaluation conducted by an experienced Amazon employee (not necessarily from your immediate team) whose role is to evaluate you against Amazon's standards and ensure hiring bar is maintained consistently across the organization. This round focuses on your alignment with Amazon's 16 Leadership Principles and your problem-solving approach to ambiguous or complex situations. You'll discuss past experiences using the STAR method (Situation, Task, Action, Result) and demonstrate how you embody principles like Customer Obsession, Ownership, and Learn and Be Curious. The Bar Raiser assesses whether you raise the bar for the team through your values and approach.[2][3]
Tips & Advice
Prepare 5-7 strong STAR stories demonstrating different Leadership Principles. For entry-level candidates, use academic projects, internships, group projects, hackathons, or personal initiatives—professional work experience isn't necessary at this level. Structure each story clearly: Situation (what was the context and challenge), Task (what were you asked to do or what did you take on), Action (what specifically did you do), Result (what was the outcome and what did you learn). Practice telling stories in 2-3 minutes concisely. Prepare stories showing: taking initiative, solving problems through learning, learning from failures, collaborating effectively, and delivering results. Connect your stories explicitly to Amazon Leadership Principles during the interview. For entry-level, the Bar Raiser understands you're early in your career; they're evaluating your values, growth mindset, learning ability, and how you approach challenges, not decades of professional experience. Be honest and authentic; made-up stories don't resonate.[2][3]
Focus Topics
Amazon Leadership Principle: Invent and Simplify
Prepare a story showing you either invented a solution to a problem or simplified an existing process. This could involve finding a more efficient way to do something, creating a tool or script to automate tedious work, thinking creatively about a challenging problem, or fundamentally rethinking an approach. For entry-level, include academic projects where you innovated: Did you create a tool that other students used? Did you simplify a process in a class or club? Did you find a creative solution to a difficult problem? Discuss your reasoning for the innovation and the results.[2][3]
Practice Interview
Study Questions
Amazon Leadership Principle: Ownership
Prepare a story showing you took ownership of a problem or project beyond what was expected of you. Ownership means taking initiative without being asked, seeing something through to completion, and taking responsibility for both successes and failures. For entry-level, examples might include: leading a group project when no one else stepped up, volunteering for a difficult task that others avoided, independently solving a problem without asking for help, or taking responsibility for a failure and fixing it. Discuss what you learned and how you'd approach similar situations differently in the future.[2][3]
Practice Interview
Study Questions
Amazon Leadership Principle: Customer Obsession
Prepare a story demonstrating how you prioritize customer or user needs in your work. This could involve identifying a user problem and solving it, going out of your way to understand what users needed, or making a decision based on user benefit rather than convenience. For entry-level, use academic or personal projects: Did you build a tool that solved a real problem for classmates? Did you improve a process because it helped users? Discuss how you put yourself in the user's shoes and made decisions based on their needs, even if it took more effort. Connect this to how you'd approach data engineering: thinking about who consumes the data you create and ensuring it's accurate and accessible.[2][3]
Practice Interview
Study Questions
Structured Behavioral Responses Using STAR Method
Master the STAR framework for telling behavioral stories: Situation (set the context and describe the challenge you faced), Task (explain your role and what you were responsible for or what you took on), Action (describe specifically what you did to address the situation—focus on your personal contributions), Result (explain the outcome, ideally with measurable results, and what you learned). Practice structuring responses this way so they're clear, compelling, and easy for the interviewer to follow. Time your stories to 2-3 minutes, being efficient without rushing. Practice pivoting the same stories to answer various Leadership Principle questions by emphasizing different aspects. For entry-level, honest, clear stories about learning and growth resonate better than overly polished stories.[2][3]
Practice Interview
Study Questions
Onsite Interview - HR and Manager Round
What to Expect
This final 30-45 minute round is typically conducted by your potential hiring manager or an HR representative. The focus shifts from technical assessment to team fit, work style compatibility, learning ability, and how you'd succeed within their specific team. The manager wants to assess whether you'll collaborate effectively, ask for help when needed (important for entry-level), grow within the role, and thrive in their team environment. This round is equally your opportunity to evaluate whether the team, manager, and role align with your career goals and work preferences. Unlike technical rounds, this is a two-way conversation.[1][2][4]
Tips & Advice
Come prepared with thoughtful questions about the role, team structure, growth opportunities, day-to-day work, and challenges. Show genuine curiosity about how the team operates and what success looks like in the role. Be authentic about your experience level as an entry-level candidate; managers appreciate honesty and enthusiasm far more than overselling yourself. Emphasize your eagerness to learn, coachability, and willingness to take on new challenges. Ask about mentorship and learning opportunities, signaling you're invested in growing in the role. Provide honest answers about your work style, how you handle feedback, how you prefer to collaborate, and how you stay motivated. For entry-level candidates, emphasizing learning ability, adaptability, and genuine enthusiasm often matters more than years of experience. Ask the manager about their leadership style, team culture, biggest current challenges, and what they're looking for in a new team member. Remember, this round is also your evaluation of the manager and team—ensure they're the right fit for you.[1][2][4]
Focus Topics
Career Goals and Fit with Amazon and This Role
Articulate your career goals in data engineering and explain why Amazon is a good fit for your growth. Discuss what excites you about this specific role and team. For entry-level candidates, it's completely fine if long-term goals are still forming—focus on what you want to learn and achieve in the next 2-3 years. Discuss why you're interested in data engineering as a career path. Explain what attracts you to Amazon specifically: the scale, the technology, the impact, the culture, specific products you're interested in. Discuss how you see yourself growing in this role over 1-2 years.[1][2]
Practice Interview
Study Questions
Work Style, Time Management, and Adaptability
Discuss your work style: how you organize tasks, manage time, prioritize when faced with competing deadlines, and handle ambiguous situations (common in data engineering). Discuss your preferences for receiving feedback and your response to criticism or course correction. For entry-level candidates, emphasize flexibility and willingness to adapt to the team's way of working rather than expecting the team to adapt to you. Discuss how you handle situations where priorities change mid-project or requirements aren't clear upfront. Mention your approach to proactively communicating status, blockers, and progress.[1][4]
Practice Interview
Study Questions
Learning Ability and Growth Mindset
Demonstrate that you're a continuous learner who actively seeks to grow. Share examples of skills you've learned recently, challenges you've overcome through learning, or areas you're currently developing. Discuss online courses you've taken (Udemy, Coursera), books or technical blogs you read, or personal projects where you had to learn new technologies. For entry-level, emphasizing your learning trajectory and growth mindset is more valuable than current expertise. Discuss how you approach learning new programming languages, frameworks, or domains: Do you prefer guided courses, hands-on projects, or learning from experienced colleagues? Discuss a time when you had to learn something outside your comfort zone and how you approached it.[2][3]
Practice Interview
Study Questions
Team Collaboration and Communication Style
Discuss how you work effectively in teams, handle disagreements respectfully, and communicate with teammates at different levels. Share examples of successful collaboration from academic projects, internships, or personal projects. For entry-level candidates, emphasize your openness to feedback, willingness to learn from experienced colleagues, and appreciation for mentorship. Discuss how you ask for help when stuck (important for entry-level roles), how you contribute ideas respectfully, and how you celebrate teammates' successes. Explain your approach to giving and receiving feedback. Discuss how you adapt your communication style based on your audience (technical vs. non-technical teammates).[1][2]
Practice Interview
Study Questions
Frequently Asked Data Engineer Interview Questions
Sample Answer
Sample Answer
Sample Answer
Sample Answer
Sample Answer
Sample Answer
Sample Answer
DO $$
DECLARE
rows_deleted INT;
BEGIN
LOOP
-- delete up to 10k rows that are older than 90 days
WITH old AS (
SELECT id FROM logs
WHERE ts < now() - INTERVAL '90 days'
ORDER BY id
LIMIT 10000
)
DELETE FROM logs l
USING old
WHERE l.id = old.id
RETURNING 1 INTO rows_deleted;
IF NOT FOUND THEN
EXIT;
END IF;
-- short pause (optional) to let replication catch up
PERFORM pg_sleep(0.1);
END LOOP;
END;
$$ LANGUAGE plpgsql;DELETE FROM logs
WHERE id IN (
SELECT id FROM logs
WHERE ts < now() - INTERVAL '90 days'
LIMIT 10000
);Sample Answer
Sample Answer
Sample Answer
Recommended Additional Resources
- LeetCode Premium - SQL and Medium-level Data Structures problems
- StrataScratch - Real SQL interview questions from companies like Amazon
- HackerRank Data Engineer path - Guided practice
- DataInterview.com - Amazon-specific SQL and data engineering questions
- InterviewQuery - Data engineering interview guides and practice questions
- Udemy: 'The Ultimate Hands-On Hadoop' and 'Apache Spark with Scala'
- Coursera: 'Data Warehouse Concepts, Design, and Data Integration' by University of Colorado
- YouTube: FreeCodeCamp 'Data Engineering with Python' and 'Complete Hadoop and Spark'
- Book: 'Designing Data-Intensive Applications' by Martin Kleppmann
- Book: 'The Data Warehouse Toolkit' by Ralph Kimball (star schema patterns)
- AWS Skill Builder - Free tier learning for S3, Redshift, Glue, EMR
- Amazon Leadership Principles guide at amazon.jobs
- Pramp or Interview.com - Free mock interviews with real engineers
- Blind and Glassdoor - Read Amazon Data Engineer interview experiences from other candidates
Search Results
Amazon Data Engineer Interview Guide 2024 | Questions, Process
Read this comprehensive guide for valuable insights and tips to ace your Amazon data engineer interview with interview questions, process and timeline.
Amazon Data Engineer Interview in 2025 (Leaked Questions)
This comprehensive guide will provide you with insights into Amazon's interview process, the essential skills required, and strategies to help you excel.
Amazon Data Engineer Interview: Inside the SQL, ETL, and ...
This guide walks you through everything you need to prepare for the Amazon data engineer interview. You'll learn what the role entails, what each interview ...
The 2025 Amazon Data Engineer interview guide | Prepfully
Detailed, specific guidance on the Amazon Data Engineer interview process - with a breakdown of different stages and interview questions asked at each stage.
Interview preparation for data roles - Amazon.jobs
Interviews for Data Engineer and Business Intelligence Engineer roles assess your technical acumen through a combination of verbal and coding exercises.
Amazon Data Engineer Interview (questions, process, prep)
We've put together the ultimate guide, complete with 50+ example questions and insider advice from our expert Amazon coaches Jitesh, Bilwasiva, and Dessy.
Amazon Data Engineer Interview Guide | Sample Questions (2025)
Prepare for the Amazon Data Engineer interview with an inside look at the interview process and sample questions. Learn how to get a Data Engineer job at ...
This interview preparation guide was generated using AI-powered research from the sources listed above. While we strive for accuracy, we recommend verifying critical information from official company sources.
Want to create your own tailored preparation guide using our deep research?
Get Started for FreeInterview-Ready Courses
Visual-first, interactive, structured learning paths