Amazon Data Engineer Interview Preparation Guide - Junior Level
Amazon's Data Engineer interview process for junior-level candidates consists of 6 rounds spanning approximately 4-6 weeks. The process begins with a recruiter screening, followed by a technical phone screen focusing on SQL and data modeling fundamentals. Candidates who advance proceed to a 4-round onsite interview loop (conducted virtually or in-person) that evaluates technical depth through two dedicated technical rounds, system-level design thinking, and behavioral alignment with Amazon's Leadership Principles. The entire process assesses both coding proficiency and ability to reason about data systems at scale.
Interview Rounds
Recruiter Screening
What to Expect
This is your first interaction with Amazon's recruiting team. The recruiter will evaluate your background, motivation for joining Amazon, and general fit for the Data Engineer role. They'll also confirm your availability and answer questions about the role and company. This round is primarily conversational and aims to validate that you meet baseline qualifications and are genuinely interested in the position.
Tips & Advice
Prepare a concise 1-2 minute 'Tell me about yourself' story that highlights relevant data engineering projects, your motivation for the role, and why Amazon specifically appeals to you. Research Amazon's data engineering culture and mention specific products or initiatives that interest you. Be honest about your technical background and avoid overstating experience. Ask thoughtful questions about the role, team structure, and data engineering challenges at Amazon. Remember that recruiters assess cultural fit and enthusiasm, not deep technical knowledge, so be personable and show genuine curiosity.
Focus Topics
Amazon Leadership Principles Overview
Familiarize yourself with Amazon's 16 Leadership Principles, particularly 'Learn and Be Curious,' 'Deliver Results,' 'Dive Deep,' and 'Ownership.' Be ready to briefly discuss how you embody at least 2-3 of these principles through past experiences. For junior level, focus on examples that show learning ability and taking initiative in your previous roles.
Practice Interview
Study Questions
Technical Background Validation
Be prepared to discuss your hands-on experience with SQL, data modeling, ETL processes, and any big data or cloud technologies. For junior level, focus on depth in one or two areas rather than breadth across many. Mention coursework, personal projects, or production systems you've contributed to. Be honest about gaps and emphasize your learning trajectory.
Practice Interview
Study Questions
Professional Background and Motivation
Develop a compelling narrative about your data engineering experience, including specific projects you've worked on, technologies you've used (SQL, ETL tools, cloud platforms), and measurable outcomes you've delivered. For junior level, focus on hands-on contributions rather than leadership. Explain why you're interested in data engineering specifically and why Amazon is the right fit for your career growth.
Practice Interview
Study Questions
Technical Phone Screen
What to Expect
The technical phone screen is a 60-minute interview conducted by a data engineer on Amazon's team. This round focuses on validating your SQL skills, understanding of data modeling, and ability to think through real-world data scenarios. You'll be asked to write SQL queries to solve specific business problems, design a simple data model, and explain your reasoning. The interviewer is assessing both your technical accuracy and your ability to communicate your thought process clearly.
Tips & Advice
Start by clarifying the problem statement—ask questions to ensure you understand the business context and data requirements before writing code. For SQL problems, think aloud as you construct your query, mentioning the joins, aggregations, and filters you'll use. Test your logic by walking through an example with sample data. For data modeling questions, draw out the schema on the whiteboard or in your notes, explaining why you chose certain field types and relationships. Keep your solutions simple and correct first, then optimize. Use standard SQL syntax and avoid database-specific functions unless the problem explicitly allows them. Practice writing queries quickly and accurately under time pressure.
Focus Topics
Basic ETL and Data Pipeline Concepts
Understand the fundamentals of Extract, Transform, Load (ETL) processes. Be familiar with common data transformation patterns such as filtering, aggregation, joining, and data type conversions. Discuss real-world scenarios: how would you extract data from a source system, apply business logic transformations, and load into a warehouse? For junior level, focus on conceptual understanding and simpler pipelines rather than complex orchestration.
Practice Interview
Study Questions
Problem-Solving Approach and Communication
Develop a structured approach to solving technical problems: clarify requirements, state assumptions, outline your solution, code it, and validate with examples. Practice explaining your reasoning out loud in a clear, logical sequence. For junior level, the interviewer is also assessing your ability to learn and ask clarifying questions when stuck.
Practice Interview
Study Questions
Data Modeling and Schema Design
Learn to design normalized and denormalized data models based on business requirements. Understand when to use fact tables, dimension tables, and slowly changing dimensions. For junior level, focus on designing simple 3-4 table schemas that support common business queries. Practice translating business problems into logical data models, choosing appropriate data types, and defining primary/foreign keys and constraints.
Practice Interview
Study Questions
SQL Fundamentals and Intermediate Queries
Master writing SQL queries involving SELECT, WHERE, GROUP BY, HAVING, ORDER BY, JOIN (INNER, LEFT, RIGHT, FULL OUTER), and aggregate functions (COUNT, SUM, AVG, MAX, MIN). For junior level, ensure comfort with basic to intermediate complexity queries. Be able to write correlated subqueries and basic CTEs (Common Table Expressions). Understand query performance implications of different join types and aggregation strategies.
Practice Interview
Study Questions
Onsite Technical Interview Round 1 - SQL & Data Modeling Deep Dive
What to Expect
In this 60-minute onsite round, you'll tackle 1-2 complex SQL and data modeling problems designed to test deeper technical proficiency. The interviewer may present business scenarios and ask you to design data schemas, write optimized SQL queries, or explain query performance trade-offs. You'll be expected to consider real-world constraints such as data volume, query patterns, and storage efficiency. This round goes deeper than the phone screen, testing your ability to apply data modeling principles to real database scenarios and optimize for query performance.
Tips & Advice
Come prepared with a clear methodology for data modeling: identify entities, determine relationships, normalize appropriately, and consider denormalization trade-offs. For SQL problems, start with a correct solution, then optimize by discussing index strategies, query rewriting, or alternative approaches. Ask the interviewer about data volume and query patterns to inform your optimization strategy. Be ready to defend your design choices—explain why you chose a certain schema or query approach over alternatives. For junior level, it's acceptable to discuss multiple approaches and ask for feedback; this shows learning mindset. Use actual SQL syntax and be precise with your solution.
Focus Topics
Real-World Data Modeling Scenarios
Practice designing data models for realistic business scenarios such as e-commerce (products, orders, inventory), advertising (campaigns, impressions, clicks), or logistics (shipments, deliveries, routes). Learn to ask clarifying questions about scale, query patterns, and business requirements before designing. For junior level, design simple-to-moderate complexity systems and be able to discuss trade-offs in your design choices.
Practice Interview
Study Questions
Data Warehousing Concepts and Best Practices
Understand star schema and snowflake schema patterns, slowly changing dimensions (SCD types 1-3), fact table design (additive, semi-additive, non-additive measures), and the difference between OLTP and OLAP systems. Learn dimensional modeling principles and how they differ from transactional schemas. For junior level, be familiar with these concepts and able to apply them to design problems.
Practice Interview
Study Questions
Advanced SQL Query Optimization
Build on SQL fundamentals to write queries that consider performance optimization. Understand window functions (ROW_NUMBER, RANK, LAG, LEAD), analytical functions, and how to write efficient aggregations. Learn to recognize and avoid common performance pitfalls such as unnecessary joins, subquery inefficiencies, or missing WHERE clause predicates. For junior level, focus on identifying optimization opportunities in moderately complex queries and explaining trade-offs between clarity and performance.
Practice Interview
Study Questions
Database Management and Query Performance Analysis
Understand indexing strategies (primary, secondary, composite indexes), how to read query execution plans to identify bottlenecks, and basic query tuning techniques. Learn about database statistics, cardinality estimation, and how query optimizers make decisions. Discuss how to identify slow queries and systematically improve them. For junior level, focus on recognizing common performance issues and proposing solutions rather than deep database internals.
Practice Interview
Study Questions
Relational Data Modeling and Normalization
Deeply understand database normalization (1NF through 3NF and BCNF), when and why to denormalize, and the trade-offs between different approaches. Learn to recognize data anomalies and design schemas that minimize them. Understand concepts like slowly changing dimensions (SCD), fact and dimension tables, and how to model relationships (one-to-many, many-to-many). For junior level, be comfortable designing normalized schemas for moderately complex business domains.
Practice Interview
Study Questions
Onsite Technical Interview Round 2 - Data Pipelines & Big Data Systems
What to Expect
This 60-minute round focuses on your understanding of ETL pipeline design, big data technologies, and AWS data services. The interviewer will present a business problem and ask you to design an end-to-end data pipeline that ingests raw data, transforms it according to business logic, and makes it available for consumption. You'll discuss technology choices (Spark vs. batch processing, partitioning strategies, orchestration approaches) and explain how to handle scale, reliability, and data quality. This round evaluates your ability to think about data systems holistically beyond just writing queries.
Tips & Advice
Start by understanding the problem fully: What is the source data? What transformations are needed? What is the query pattern? What scale are we operating at? Ask about data volume, velocity, and latency requirements—these drive technology choices. For junior level, propose reasonable solutions even if you're not deeply familiar with every tool; explain your thinking and be open to feedback. Discuss tradeoffs: cost vs. performance, complexity vs. maintainability, batch vs. real-time processing. Mention AWS-specific services like AWS Glue, AWS Lambda, or AWS Step Functions if appropriate. Draw diagrams showing data flow through your pipeline. Be prepared to discuss data quality checks, error handling, and monitoring.
Focus Topics
Big Data Technologies - Apache Spark and Hadoop
Understand the fundamentals of distributed computing with Apache Spark and Hadoop ecosystems. Learn Spark's architecture (driver, executors, partitions), RDDs and DataFrames, and basic operations like map, filter, reduce, and aggregation. Understand when to use Spark vs. SQL-based tools. Discuss partitioning strategies and how they impact performance. For junior level, be familiar with Spark concepts and able to discuss simple transformations; hands-on coding experience is a plus but understanding and explanation are primary.
Practice Interview
Study Questions
Data Quality, Validation, and Error Handling
Learn to design data quality checks into pipelines: validate row counts, check for nulls, verify data types, detect duplicates, and validate business logic constraints. Understand how to implement monitoring and alerting for data quality issues. Discuss how to handle errors gracefully: retries, dead letter queues, and manual intervention. For junior level, be able to articulate common data quality issues and how to detect and handle them.
Practice Interview
Study Questions
ETL Pipeline Design and Implementation
Understand the full lifecycle of ETL processes: extracting data from various sources (databases, APIs, logs), applying business logic transformations (filtering, enrichment, aggregation, validation), and loading into target systems. Learn to design pipelines that handle incremental data, backfills, and data corrections. Discuss error handling, retry logic, and recovery strategies. For junior level, be able to design and discuss simple-to-moderately-complex ETL workflows and explain how to implement them using available tools.
Practice Interview
Study Questions
Data Pipeline Architecture and Data Flow
Learn to design overall data architectures that connect data sources to consumers. Understand concepts like data ingestion layers, processing layers, and storage layers. Discuss how data flows through your architecture and where transformations occur. Learn about lambda architecture (batch + real-time processing) and different pipeline orchestration approaches. For junior level, be comfortable designing simple 3-4 layer architectures and explaining component interactions.
Practice Interview
Study Questions
AWS Data Services and Tools
Become familiar with Amazon's data tools and services: AWS Glue (ETL service), AWS Redshift (data warehouse), AWS EMR (managed Hadoop/Spark), AWS Lambda (serverless compute), AWS Step Functions (workflow orchestration), Amazon S3 (object storage), and AWS Athena (query service). Understand when to use each service, how they integrate, and their trade-offs. For junior level, focus on understanding use cases and basic capabilities rather than deep implementation details.
Practice Interview
Study Questions
Onsite System Design Round - Scalable Data Architecture
What to Expect
In this 60-minute round, you'll work with an interviewer to design a large-scale data system for a realistic scenario. For example, you might be asked to design a data lake for streaming analytics, a metrics pipeline for a product feature, or an ingestion system for high-volume transactional data. The focus is on understanding how to scale data systems, make technology trade-offs, and think about reliability and cost. The interviewer will probe your decisions and challenge your assumptions, guiding you to think through second and third-order implications of your design.
Tips & Advice
For junior level, you're not expected to design perfect systems, but to show structured thinking. Start by clarifying requirements: volume, latency, consistency needs, and cost constraints. Build your design in layers: data ingestion, processing/transformation, storage, and consumption. For each layer, discuss technology options and trade-offs. Draw architecture diagrams showing data flow. Discuss how you'd handle scale—what happens when data volume 10x? Discuss failure scenarios and how you'd recover. Talk about monitoring and alerting. Be explicit about your assumptions and willing to revisit them if challenged. If you don't know something, acknowledge it and explain how you'd approach learning it.
Focus Topics
Partitioning Strategies and Data Organization
Learn how to partition data for efficient querying and processing: time-based partitioning, hash partitioning, range partitioning, and directory-based partitioning in data lakes. Understand the impact of partitioning on query performance and storage efficiency. Discuss how to design partition schemes that support common query patterns. For junior level, be able to propose reasonable partitioning strategies for simple scenarios.
Practice Interview
Study Questions
Data Ingestion and Source System Integration
Understand different data ingestion patterns: batch ingestion from databases, streaming ingestion from message queues, log collection from applications, and API-based ingestion. Discuss how to handle different source system characteristics: frequency, volume, formats. Learn about connectors and integration frameworks. For junior level, be comfortable discussing ingestion design for 2-3 different source types and trade-offs between approaches.
Practice Interview
Study Questions
Fault Tolerance, Reliability, and Monitoring
Discuss how to build reliable systems that continue operating even when components fail. Understand replication, backup strategies, and recovery procedures. Learn about monitoring and alerting to detect issues early. Discuss SLAs and how to design systems to meet them. For junior level, be aware of common failure modes and how to design systems to detect and handle them gracefully.
Practice Interview
Study Questions
Scalable Data Architecture Design
Learn to design data systems that scale from gigabytes to terabytes or more. Understand how to partition data for parallel processing, distribute computation across clusters, and manage state in distributed systems. Discuss trade-offs between consistency, availability, and partition tolerance (CAP theorem at a high level). For junior level, focus on designing systems that handle 10-100x growth from a baseline and explaining how architectural choices support scaling.
Practice Interview
Study Questions
Onsite Behavioral & Leadership Round
What to Expect
This 60-minute round assesses your alignment with Amazon's Leadership Principles and your interpersonal skills through behavioral questions. The interviewer will ask about past experiences, challenges you've overcome, how you work in teams, and your approach to problems. They'll listen for evidence of principles like 'Learn and Be Curious,' 'Deliver Results,' 'Dive Deep,' 'Ownership,' and 'Teamwork.' For junior-level candidates, the focus is on your learning ability, willingness to ask for help when needed, and foundational demonstration of these principles through past roles and projects.
Tips & Advice
Prepare 6-8 STAR-format stories (Situation, Task, Action, Result) from your past experiences that demonstrate different Amazon Leadership Principles. For junior level, draw from academic projects, internships, coursework, or entry-level work experience. Structure each story to be 2-3 minutes: describe the situation and challenge, explain what you did (your actions and thinking), and share measurable results or learnings. Practice delivering these stories concisely and naturally. Prepare examples that show: learning something new, taking ownership of a problem, working effectively with teammates, handling a setback or failure, and delivering against a deadline. When answering questions, be specific and avoid generic responses. Use metrics and outcomes when possible. Show curiosity and mention times you asked questions or sought feedback. For junior level, it's perfectly fine to discuss learnings and 'here's what I'd do differently' rather than claiming perfect execution.
Focus Topics
Handling Ambiguity, Challenges, and Learning from Failure
Prepare stories that show how you've approached problems when requirements were unclear, handled technical challenges, or failed and learned from it. For junior level, discuss situations where you had to ask clarifying questions, seek guidance, or research a new technology to solve a problem. Show a learning mindset—explain what you learned and how you'd handle similar situations differently. Discuss a time when you failed and what insights you gained.
Practice Interview
Study Questions
Teamwork and Collaboration in Data Engineering
Prepare examples that demonstrate your ability to work effectively with data scientists, analysts, and other engineers. Discuss how you've handled conflicting priorities, navigated ambiguity with teammates, and supported others' success. Share stories about asking for help, receiving feedback, and improving based on it. Discuss how you ensure data systems meet the needs of downstream users. For junior level, show willingness to collaborate, listen to feedback, and learn from more experienced colleagues.
Practice Interview
Study Questions
Amazon Leadership Principles - Deep Dive
Thoroughly understand Amazon's 16 Leadership Principles and how they apply to the data engineer role. Focus particularly on: 'Learn and Be Curious' (initiative to develop expertise, asking questions), 'Deliver Results' (execution, persistence, holding yourself accountable), 'Dive Deep' (detail orientation, understanding root causes), 'Ownership' (long-term thinking, accountability beyond your immediate scope), 'Are Right, a Lot' (good judgment, learning from mistakes), and 'Earn Trust' (collaborative approach, high standards). Prepare stories that clearly demonstrate each principle through concrete examples. For junior level, stories should show emerging capability in these areas, not mastery.
Practice Interview
Study Questions
Frequently Asked Data Engineer Interview Questions
Sample Answer
Sample Answer
Sample Answer
Sample Answer
Sample Answer
-- 1. Aggregate order-level metrics
WITH order_aggr AS (
SELECT
o.id AS order_id,
DATE_TRUNC('month', o.created_at) AS month,
o.customer_id,
o.total_amount,
o.promotion_id,
COUNT(oi.id) AS items_in_order
FROM orders o
JOIN order_items oi ON oi.order_id = o.id
GROUP BY 1,2,3,4,5
),
-- 2. Compute revenue per order item-category for top_category calculation
category_revenue AS (
SELECT
DATE_TRUNC('month', o.created_at) AS month,
p.category AS category,
SUM(oi.price * oi.quantity) AS revenue
FROM orders o
JOIN order_items oi ON oi.order_id = o.id
JOIN products p ON p.id = oi.product_id
GROUP BY 1,2
),
-- 3. Rank categories per month to pick top category
ranked_categories AS (
SELECT
month,
category,
revenue,
ROW_NUMBER() OVER (PARTITION BY month ORDER BY revenue DESC) AS rn
FROM category_revenue
),
-- 4. Aggregate monthly KPIs
monthly_metrics AS (
SELECT
oa.month,
SUM(oa.total_amount) AS total_revenue,
COUNT(DISTINCT oa.order_id) AS total_orders,
CASE WHEN COUNT(DISTINCT oa.order_id) = 0 THEN 0
ELSE SUM(oa.total_amount) / COUNT(DISTINCT oa.order_id) END AS avg_order_value,
SUM(oa.items_in_order) AS total_items,
COUNT(DISTINCT oa.customer_id) AS unique_customers,
SUM(CASE WHEN oa.promotion_id IS NOT NULL THEN oa.total_amount ELSE 0 END) AS promo_revenue
FROM order_aggr oa
GROUP BY oa.month
)
-- 5. Put it together into the denormalized table
SELECT
m.month,
m.total_revenue,
m.total_orders,
m.avg_order_value,
m.total_items,
m.unique_customers,
m.promo_revenue,
rc.category AS top_category
INTO monthly_reporting_kpis -- or use CREATE TABLE AS SELECT depending on your SQL engine
FROM monthly_metrics m
LEFT JOIN ranked_categories rc
ON rc.month = m.month AND rc.rn = 1
ORDER BY m.month;Sample Answer
Sample Answer
Sample Answer
WITH to_delete AS (
SELECT ctid
FROM (
SELECT ctid,
ROW_NUMBER() OVER (PARTITION BY event_id ORDER BY event_ts DESC, ctid) AS rn
FROM events
) t
WHERE rn > 1
)
DELETE FROM events e
USING to_delete d
WHERE e.ctid = d.ctid;CREATE TABLE events_cleaned AS
SELECT DISTINCT ON (event_id) event_id, payload, event_ts
FROM events
ORDER BY event_id, event_ts DESC;
-- Optionally swap names
BEGIN;
DROP TABLE events_old;
ALTER TABLE events_cleaned RENAME TO events;
COMMIT;Sample Answer
SELECT event_date, COUNT(DISTINCT user_id) AS dau
FROM events
WHERE product_id = 'X'
AND user_id IS NOT NULL
AND event_time >= '2025-01-01' -- sample window
GROUP BY event_date
ORDER BY event_date;Sample Answer
Recommended Additional Resources
- LeetCode SQL problems and medium-difficulty data engineering interview questions
- DataInterview.com: Real Amazon data engineer interview questions and mock interviews
- InterviewQuery.com: Comprehensive data engineering interview preparation platform with system design resources
- AWS official documentation: AWS Glue, AWS Redshift, AWS EMR, and AWS Lambda for hands-on learning
- Apache Spark official documentation and tutorials for distributed computing concepts
- SQL optimization guides and database indexing strategies documentation
- Amazon Leadership Principles: Read the official descriptions and practice answering questions using STAR method
- Glassdoor Amazon Data Engineer reviews for recent candidate feedback on interview experience
- YouTube: Search 'Amazon data engineer interview walkthrough' for real interview experiences shared by candidates
- Cracking the Data Engineering Interview by Elijah Meeus or similar recent data engineering interview prep books
- Mock interview platforms: Pramp, Interviewing.io for real-time practice with other engineers
- Amazon's official careers page (amazon.jobs) for product domain knowledge and data engineering role descriptions
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