Amazon Data Engineer Interview Guide

Amazon's loop is defined by the Leadership Principles: every round, including the technical ones, probes behavioral signal, and a Bar Raiser sits in to guard the hiring bar. The technical screen mixes live SQL and data-transformation Python, with SQL emphasizing window functions and CTEs and Python framed as data processing rather than competitive programming. Expect star-schema and SCD modeling questions and a strong focus on partitioning and query cost.

Last updated: August 2, 2026Proudly published by: Jeff Wahl

The Amazon interview timeline

First contact to offer, stage by stage, with how long each round runs and roughly when it lands.

1
wk 170 to 90 min
Online Assessment (OA)
2
wk 1-245 to 60 min
Phone Screen
3
wk 345 to 60 min
Onsite: SQL Deep Dive
4
wk 345 to 60 min
Onsite: System Design / Pipeline Architecture
5
wk 345 to 60 min
Onsite: Behavioral / Leadership Principles
6
wk 345 to 60 min
Onsite: Bar Raiser

Typical Amazon loop, first contact to offer. Week estimates are approximate and vary by team and scheduling.

Amazon data engineer interview process

The loop stage by stage, from recruiter call to offer.

01
Online Assessment (OA)
Many Amazon DE roles start with an online assessment: 1 to 2 SQL problems and sometimes a Python coding problem on a proctored platform. The SQL covers aggregation, joins, and window functions on Amazon-like schemas (orders, shipments, inventory, customer reviews). The difficulty is moderate but you are timed, and there is no partial credit. Some roles skip the OA entirely and go straight to the phone screen.
- ▸Practice timed SQL. The OA gives you roughly 30 minutes per SQL question
- ▸Read the problem statement twice; Amazon OA questions bury subtle constraints in the description
- ▸If there is a Python component, expect data manipulation (parsing, transforming dictionaries, file processing), not algorithms
- ▸Test your solution against the provided examples, then think about edge cases before submitting
02
Phone Screen
A video call with a data engineer from the hiring team: typically 30 to 35 minutes of technical questions (SQL and possibly Python) followed by 10 to 15 minutes of behavioral questions tied to Leadership Principles. The technical portion is harder than the OA. Expect multi-step SQL involving window functions, self-joins, and date arithmetic. The interviewer will ask you to explain your approach before you write code.
- ▸Explain your approach before writing SQL; Amazon interviewers assess your thinking, not just the final query
- ▸For behavioral questions, use STAR and name the Leadership Principle your answer demonstrates
- ▸If asked 'What would you do differently next time?', they are assessing self-awareness, not criticism
- ▸Prepare for data-quality questions; Amazon cares deeply about accuracy because it affects customer experience
03
Onsite: SQL Deep Dive
The most technically demanding SQL round in the loop. 2 to 3 problems of increasing difficulty, often set in Amazon contexts (order fulfillment, inventory tracking, seller performance, delivery estimates). The interviewer expects clean, efficient SQL and a discussion of optimization. After solving a problem you may be asked: 'This table has 10 billion rows. How would you make this query fast?'
- ▸Amazon schemas often include timestamps, status columns, and hierarchical categories. Practice time-based aggregation and status transitions
- ▸For optimization, mention partitioning by date, indexing on join columns, and avoiding SELECT * on wide tables
- ▸You may be asked to rewrite a correlated subquery as a join or vice versa. Know both approaches
04
Onsite: System Design / Pipeline Architecture
Design a data pipeline or platform component for an Amazon use case. Common prompts: real-time order-tracking analytics, seller performance monitoring, a recommendation-engine data pipeline, or an inventory-forecasting platform. You are expected to drive the conversation, sketch architecture, estimate data volumes, and discuss monitoring and alerting.
- ▸Start by clarifying requirements: latency SLA, data volume, consumers, and what 'correct' means for this use case
- ▸Amazon loves operational excellence. Include monitoring, alerting, runbooks, and auto-recovery in your design
- ▸Name AWS services where appropriate but explain why you chose them over alternatives
05
Onsite: Behavioral / Leadership Principles
A full round dedicated to behavioral questions, each mapped to specific Leadership Principles. The interviewer explicitly probes situations that demonstrate Customer Obsession, Ownership, Dive Deep, Bias for Action, and Earn Trust. Some interviewers cover 3 to 4 principles in one round, asking follow-ups that probe the depth and authenticity of your examples.
- ▸Prepare 2 stories per Leadership Principle so you have backups when one story does not fit the specific question
- ▸Quantify every result: latency reduction, cost savings, pipeline uptime, data-freshness improvement
- ▸Be honest about failures. Admitting a mistake with lessons learned is stronger than pretending everything went perfectly (Earn Trust)
06
Onsite: Bar Raiser
The Bar Raiser is a specially trained interviewer from outside the hiring team. Their job is to evaluate whether you raise the bar for Amazon overall, not just whether you can do this specific job. The round mixes technical and behavioral questions, and the Bar Raiser has authority to veto a hire even if all other interviewers say yes.
- ▸Treat this round with the same preparation as any other; the Bar Raiser is more experienced at detecting rehearsed or inflated answers
- ▸The Bar Raiser often asks 'Why?' repeatedly to probe depth. Have genuine understanding behind every claim on your resume
- ▸If they pivot to a topic you did not expect, stay calm and think out loud. They are assessing adaptability as much as knowledge

What the Amazon loop tests: domains and difficulty

Our prediction of the question mix by domain and difficulty for this company's data engineer loop, from live listings and interview reports.

By domain

SQL

62%

Python

23%

Data modeling

15%

By difficulty

Easy

54%

Medium

31%

Hard

15%

The domain and difficulty mix we predict for a Amazon data engineer loop, across 13 problems. It updates as more Amazon data lands.

Updated Aug 2, 202613 predicted Amazon problems

10 real Amazon interview questions

Reported by candidates from real loops, tagged by domain, round, level, and year. Expand for what the round is scoring.

SQLL4 · 2023

Amazon DE phone screen: 4 SQL questions escalating from simple single-table output to multi-table joins, CTEs, and window functions; recruiter confirmed SQL + data modeling + Python (no algorithms)

Phone screen · screen sql

SQLL6

Identify the top two highest-grossing products within each category for the year 2022; schema: product_spend(category VARCHAR, product VARCHAR, user_id INTEGER, spend DECIMAL, transaction_date DATETIME)

Onsite · sql

PythonL4 · 2025

Given a list of daily prices, find the maximum profit from buying and selling once, and return the buy and sell day indices

Phone screen · screen python

Write a function best_trade(prices) that returns a tuple (profit, buy_day, sell_day) representing the maximum profit achievable by buying on buy_day and selling on sell_day (where sell_day > buy_day). If no profitable trade exists, return (0, -1, -1). If multiple trades yield the same maximum profit, return the earliest buy day. Example: prices = [7, 1, 5, 3, 6, 4] Output: (5, 1, 4) (buy at index 1 for price 1, sell at index 4 for price 6) prices = [7, 6, 4, 3, 1] Output: (0, -1, -1) prices = [2, 4, 1, 7] Output: (6, 2, 3) (buy at index 2 for price 1, sell at index 3 for price 7) prices = [5…

PythonL5 · 2025

Given a JSON object with nested objects, write a function that flattens all the objects to a single key-value dictionary

Onsite · python

Amazon Data Engineer Interview Loop coding question. Given a nested JSON/dict structure like {"a": 1, "b": {"c": 2, "d": {"e": 3}}}, flatten to {"a": 1, "b.c": 2, "b.d.e": 3} using dot-separated keys. Expected approach: recursive function that traverses the dict, building up a key prefix as it descends. Must handle: nested dicts at arbitrary depth, mixed value types (ints, strings, lists), and empty nested dicts. Follow-up may ask about iterative vs recursive approach and stack overflow concerns for deeply nested objects.

Data modelingL6 · 2025

Design a data model to track a product from the vendor to the Amazon warehouse to delivery to the customer

Onsite · data modeling

Amazon Data Engineer Interview Loop data modeling question. Candidate must design an end-to-end supply chain data model with entities for: vendors, purchase_orders, warehouse_inventory, shipments, delivery_events, and customers. Expected to define: grain of each table (one row per shipment leg vs one row per product unit), surrogate keys for each entity, foreign key relationships linking the supply chain stages, and how to handle split shipments where one order goes to multiple warehouses.

Data modelingL5 · 2025

Implement a Type 2 Slowly Changing Dimension for customer profiles that preserves historical changes in attributes such as address and Prime membership status.

Onsite · data modeling

Schema design task: dim_customer table must track history of mutable attributes (shipping_address, prime_status, email). SCD Type 2 pattern requires: surrogate key (customer_sk), natural key (customer_id), effective_date, expiry_date (or is_current boolean), version_number. INSERT logic for new records, UPDATE logic to close old records (set expiry_date = current_date - 1 day, is_current = false). Interviewers follow up on: how to handle late-arriving records, index design for performance, and how downstream fact tables reference the dimension (by surrogate key not natural key).

System designL7 · 2025

How would you build a data pipeline around an AWS product that can handle increasing data volume?

Onsite · pipeline architecture

Amazon Data Engineer Interview Loop system design question targeting principal-level candidates. Open-ended question requiring discussion of: choice of AWS services (Kinesis vs MSK for ingestion, Glue vs EMR for transformation, Redshift vs Athena for querying), auto-scaling strategies for each component, partitioning and compaction strategies for S3 data lake, cost optimization at scale, and monitoring/alerting for pipeline health. Interviewer expected candidates to discuss specific throughput numbers and back-of-envelope capacity calculations. Source provided limited follow-up detail.

Behavioral / mixedL5 · 2024

Tell me about a time you resolved a data incident that impacted downstream analytics: describe how you identified the root cause, communicated with stakeholders, and implemented a fix.

Behavioral

Amazon Leadership Principle: Ownership / Deliver Results. Interviewers expect a STAR-format answer. High-scoring responses cover: (1) how the candidate detected the issue (monitoring alert, stakeholder report, data quality check), (2) root cause diagnosis (upstream schema change, late data, pipeline bug), (3) immediate mitigation (rerun, hotfix, downstream team notification), (4) long-term fix (alerting, validation checks), (5) measurable outcome (SLA recovery time, stakeholder trust). DE-specific context expected: should mention data pipeline, ETL job, or analytics dependency.

Behavioral / mixedL6 · 2024

Describe a time when you improved the scalability or cost efficiency of a data pipeline: what was the problem, what changes did you make, and what was the measurable impact?

Behavioral

Amazon Leadership Principle: Frugality / Think Big. Expected STAR answer covers: (1) context of the pipeline (batch or streaming, scale of data, business criticality), (2) what triggered the review (cost spike, SLA miss, capacity planning), (3) specific technical changes made (partitioning, compression, query optimization, right-sizing clusters, caching), (4) quantified result (cost reduction %, throughput increase, latency improvement). Interviewers at L6+ look for cross-team influence and org-level impact. DE-specific framing required.

Behavioral / mixedL5 · 2023

Tell me about a time you were in a meeting and had a different opinion from everyone else in the room; what did you do and what was the outcome?

Behavioral

Amazon Leadership Principle: Have Backbone; Disagree and Commit. Expected STAR answer: (1) describe the context and the decision being made, (2) explain your opposing view and how you backed it with data or reasoning, (3) what happened — did you escalate, present a counter-proposal, or ultimately commit to the group decision, (4) final outcome and retrospective. For a DE role, this often surfaces in discussions about technology choices (e.g., Spark vs. dbt, warehouse vs. data lake), data modeling decisions, or build vs. buy choices. From IGotAnOffer Amazon DE interview guide.

How candidates rate the Amazon loop

How hard candidates rated the loop and how they felt, summarized across the reports below.

How hard candidates rated it

Easy

25%

Medium

42%

Hard

33%

12 rated reports

How candidates felt

Positive

43%

Neutral

29%

Negative

29%

7 rated Amazon reports

Recent Amazon interview reports

Candidate accounts of the loop, each with its date, level, difficulty, and outcome. Scroll the feed.

12 candidate interview reports

real candidate submissions

No offerDifficult· Mid-levelMar 2026

the interview process went well i was approached by a recruiter and then did something or the other got on a call then went with them to do a few interviews before it did not work out

No offerDifficult· InternFeb 2026

Online assessment followed by two back-to-back 1‑hour interviews with subject matter experts. The first focused on Python (coding and problem solving), and the second primarily tested SQL concepts and query writing.

No offerDifficult· Mid-levelFeb 2026

The interview was lengthy with online problem-solving questions, experience-related questions, and two coding questions. Could do one, not both. The interviewer was also very dry and did not give a lot of attention to the interview.

Offer · acceptedAverage difficulty· Mid-levelFeb 2026

Initial Pre-screen with Recruiter Data modeling and sql for real scenarios Python Coding Bar Raiser and Leadership Principles Interview with Hiring Manger was mostly about the projects and how I handled the challenges.

No offerEasy· SeniorDec 2025

The first screening test. Based on what I read from other peoples experiences, I studied so many different things but that only asked very basic sql and mostly multi choice platform question

· Mid-levelFeb 2025

Hi community, I have my first-round interview scheduled with Amazon for the Data Engineer 1 role. The HR has mentioned the following topics in the invitation: Coding: Basic scripting using Python, Scala, Java, etc. (any one) Structured Query Language (SQL) Leadership Principles: Learn and Be Curious, Dive Deep I need some urgent guidance on the following: To what extent should I prepare for DSA and SQL?
Will it be medium-hard Leetcode-style questions, or should I expect something different? Can I expect theoretical questions related to Python/SQL in the first round, or will it be purely problem-solving? What kind of questions can I expect related to the two leadership principles mentioned?
Read full report
Do I need to be prepared for questions related to my projects and past experience? If anyone has recently given the interview for Data Engineer 1 at Amazon, please share your experience—it would be really helpful! Thanks, community! Appreciate any insights.

No offerDifficult· Mid-levelDec 2024

5 interview, with one bar raiser. First 3 are easy, the last 2 are more difficult as you'll be exhausted from the whole process. The last 2 interviews are strictly technical

No offerAverage difficulty· Mid-levelDec 2024

The interview has many stages one of which is still a mystery to me. They make you play games and do “psychological assessment” based on your game skills. If you’re not a gamer I doubt you’d get good scores there and the “assessment” then says that you’re not handling stress well.

Easy· InternDec 2024

Hi everyone, I had interviewed at Amazon a couple of months back for data engineer role and I am sharing my experience here. Before that I would like to reiterate the age old advice that corporate is not your friend. After 2 rounds of interviews which went fabulous and the interviewer telling me that it's one of the most fun interviews he has taken, I was ghosted by the organization.
I neither received a rejection nor a selection mail. I contacted the recruiter who had set up my call and she said she is out of the loop and will get back to me (spoiler alert: she never does). Anyway here's how it went for those who are interested. Round 0: Technical Assesment This was a 60 min long online assesment round where the questions asked were of simple data structure and CS Fundamentals.
Read full report
Prepare database concepts and operating systems pretty well. Round 1: First Interview: Data Structures + SQL This round was the easier of the two rounds where the interview began after an informal introduction. The first thing they threw at me was an easy leetcode question(something like balanced paranthesis) after that they asked a medium leetcode problem regarding graphs(was very standard).

Offer · accepted· Mid-levelNov 2024

I have a Data Engineer (junior/intermediate) phone interview coming up with Amazon. The position is in Canada/USA. Those who have been through it or know other people who have, what type of questions are asked in the 60-minute interview? Do they ask Leetcode-style DSA?
Read full report
If so, then what's the difficulty level of the questions? Which DSA topics are more commonly covered in an Amazon DE phone interview? What database/RDBMS/AWS questions are covered? One more thing: Would picking interview slots that are about two weeks away from now harm my chances of proceeding further into the process in any way (e.g. someone else getting hired before my interview process ends)?

· Mid-levelOct 2024

I have Amazon interview in upcoming week for Data engineer role they have told me that there will be 4 section for this interview 1> ETL 2> SQL Queries 3> Handling Big data Volume 4> LD principle ( 1of the 16 LD principle ) can someone help me what type of questions can be asked for ETL and handling big data volume section as they are very open ended topics i think.

Easy· InternSep 2024

Hi Everyone! While preparing for my data engineering intern interview at Amazon, I found very little information about the rounds and what questions are asked. So I decided to write about my own experience! Hope this helps. Role - Data Engineering Intern 6 months (Jan - May) Background - Tier 2 CSE Previous Internhip - Summer Internship at an MNC Offcampus Number of Rounds - 3 1.
Online Assesment - Technical Assesment for 1hr 30 mins, this included questions based on DS, OS, DBMS and CN. 2. Round 1 Interview - 1hour * The interview started with very simple questions to be implemented in python(they did not mention the language explicitly but I guess they expect python knowledge).
Read full report
The questions asked were balanced paranthesis and top k frequent elements both I solved quickly. * Next he went on to discuss about my project for 5-10 mins and he was interested by it so asked some more questions regarding the use case. * He then asked some questions about python decorators, lambda functions and list comprehension. * Next I was asked some SQL conceptual questions such as difference between Row(), Rank(), and DenseRank(). After this he asked 2 simple sql query questions.

Try a Amazon-style SQL round

Find every user active on 3 or more CONSECUTIVE days. This gaps-and-islands shape shows up in nearly every DE SQL round. Edit the query and run it against the seed data.

	/* Users active on 3+ consecutive days. */
	/* Hint: date minus a per-user ROW_NUMBER is constant within a streak. */
	WITH streaks AS (
	SELECT
	user_id,
	activity_date,
	activity_date - CAST(
	(ROW_NUMBER() OVER (
	PARTITION BY user_id
	ORDER BY activity_date
	))
	AS INT
	) AS grp
	FROM user_sessions
	)

	SELECT
	user_id
	FROM streaks
	GROUP BY user_id, grp
	HAVING COUNT(*) >= 3

Practice the Amazon loop

The problems our model expects in this company's interview, grouped by round. Work the shapes that come up, not the ones that read well on a list.

SQL3 to work

High Volume Batch Jobs

Python3 to work

Frequency CountingSolve →

EASY~10m

The Bitwise Judge

Solve →

Data modeling3 to work

EASY~15m

The Balance Always Reconciles

Grain & Fan TrapsSolve →

EASY~15m

Ghosts in the Ledger

Additive MeasuresSolve →

EASY~15m

Personal Best

Grain & Fan TrapsSolve →

All Amazon-style problems →

Amazon-specific preparation tips

Tactical advice for the dimensions this company weighs.

Master Amazon SQL patterns

Amazon SQL questions revolve around e-commerce schemas: orders, products, sellers, shipments, returns, and reviews. Practice time-based filtering (last 90 days, month-over-month comparisons), status transitions (ordered to shipped to delivered), and ranking (top sellers, most-returned products). Do 3 to 5 timed problems per day for 2 weeks.

Map your stories to Leadership Principles

Build a matrix: Leadership Principles on one axis, your career stories on the other. Each story should map to 2 to 3 principles. Write STAR bullets for each and practice telling them out loud in under 3 minutes. Amazon behavioral prep takes as much time as technical prep, and most candidates under-invest here.

Practice system design with AWS services

Amazon interviewers expect AWS familiarity. Saying 'Kinesis for streaming ingestion, S3 for raw storage, Glue for ETL, and Redshift for the warehouse' is far more credible than generic answers. Work through 3 to 4 common DE system-design problems and practice sketching architecture with AWS components, always explaining your choices.

Simulate the full loop

An Amazon onsite is 4 to 5 back-to-back rounds over a full day, so stamina matters. Do at least one full mock loop: 4 rounds in a row with 5-minute breaks. Notice when your energy drops and your answers get vague; that is the round you need to prepare more for.

What Amazon is really evaluating

The signals behind the questions. Shape every answer around these.

Customer Obsession

Data engineers serve internal customers: analysts, data scientists, product managers. Amazon wants to hear how you prioritized their needs, understood their pain points, and delivered data products that solved real problems. Every behavioral answer should connect back to the person who used your work.

Ownership

You built it, you own it. Amazon expects data engineers to monitor their pipelines, respond to failures, and improve reliability without being asked. Stories about taking end-to-end responsibility for a data system, including the parts that were not your formal job, land hard with interviewers.

Dive Deep

When a pipeline breaks, do you look at the error message and restart it, or investigate the root cause? Amazon wants engineers who dig into the data, question anomalies, and understand their systems at a granular level. Bring stories about finding subtle bugs that others missed.

Bias for Action

Speed matters at Amazon. They want engineers who decide with 70% of the information rather than waiting for 100%. Share examples where you shipped a V1 quickly, gathered feedback, and iterated. Analysis paralysis is a red flag in Amazon interviews.

Earn Trust

Trust comes from delivering reliably and communicating honestly. Amazon interviewers look for candidates who admit mistakes, share credit, and are transparent about tradeoffs. If your pipeline had a data-quality issue, how you communicated it matters as much as how you fixed it.

Amazon is hiring data engineers now

The roles behind this loop. Prep against the levels and locations they are actually filling.

Hiring now

Amazon data engineer · live from career pages

101

open roles

Seattle

top metro

Our engineers work directly with source systems to procure data, convert it into structured formats, build large-scale processing pipelines, design analytical data models, and maintain infrastructure with the highest security and compliance standards.

L5Seattle14d ago

About the team Sales Data Services team is part of Sales AI, a central data and science organization within Sales Intelligence, Technology, & Enablement organization (SITE) that powers Ad Sales selling motions and workflows via a suite of AI/ML services.

L4Bengaluru21d ago

Amazon is looking for a motivated individual with strong database, analytical skills and technology experience to join the DIGI (Ad Sales Finance analytics ) team.

L4Bengaluru27d ago

Design, develop, implement, test, document, and operate large-scale, high-volume, high-performance data structures for business intelligence analytics.

L5New York40d ago

Amazon Manufacturing Services (AMS) runs 135+ machines producing custom parts for over 100 Amazon organizations, and nearly every machine, order, and operator action generates data worth analyzing.

L4Bellevue49d ago

As a senior engineer, you'll be a technical leader responsible for architectural decisions and mentoring that shape both our data platforms and team members.

L5Bengaluru54d ago

Build data pipelines purpose-built for LLM consumption and create data products and feature stores that serve GenAI applications in near real-time

L5Seattle60d ago

About the team Leo Data Platform team build services to ingest, transform, and aggregate data from various devices in Leo Network, and auto detect, diagnose, and resolve issues.

L4Redmond68d ago

Own the technical quality of all team deliverables across hardware, software, and systems integration — establishing engineering standards, design review rigor, and quality gates that ensure platforms produce reliable, high-quality data.

L5Seattle68d ago

A day in the life You'll build data products that feed both human analysts and AI-powered analytics agents, leverage LLMs and generative AI tools to accelerate your own development workflows, and help move the team from manual pipeline operations toward automated, self-healing data infrastructure.

L4Seattle87d ago

New postings per week

6/22

6/29

7/6

7/13

7/20

7/27

week beginning · ~22 weeks of data

Where they hire

Seattle

Bangalore

New York

San Francisco Bay Area

Levels hiring

L45L55

Updated Aug 2, 2026101 open listings across 6 cities

Amazon compensation and culture

The numbers, tech stack, and team structure live on the company overview.

Data engineering at Amazon→

The role, comp by level, tech stack, teams, and culture signals.

Amazon salary detail→

Full compensation breakdown by level, from verified salary reports.

Amazon DE interview FAQ

How many rounds are in an Amazon DE onsite?+

Typically 4 to 5 rounds: SQL deep dive, system design or pipeline architecture, a full behavioral round, and a Bar Raiser round. Some loops include a Python coding round as well. Every round holds back at least one behavioral question tied to a Leadership Principle, so expect behavioral questions throughout the day.

What are the most important Leadership Principles for DE roles?+

Customer Obsession, Ownership, Dive Deep, Bias for Action, and Earn Trust come up most frequently in DE interviews. Ownership is particularly important because Amazon expects data engineers to monitor, maintain, and improve their pipelines without being asked. Prepare at least 2 stories for each of these 5.

Does Amazon use LeetCode-style algorithm questions for DEs?+

Rarely. Amazon DE interviews focus on SQL, data-pipeline design, and Python for data manipulation. Some Bar Raisers with SWE backgrounds may ask a basic algorithm question, but this is uncommon. If your recruiter mentions a coding round, clarify whether it is Python data manipulation or algorithm-focused so you can prep accordingly.

What is the Bar Raiser, and should I be worried about it?+

The Bar Raiser is a trained interviewer from outside the hiring team who keeps Amazon's hiring bar high, with veto power over the decision. The round is not necessarily harder than others, but the Bar Raiser is experienced at detecting inflated or rehearsed answers. Be genuine, specific, and honest. If you prepared well for the other rounds, you are prepared for the Bar Raiser.