Data Engineering at Amazon
Roles, Comp & Culture
An L7 principal data engineer at Amazon sits around $517K total comp from 3124 verified salary datapoints. The primary Data Engineering tech consists of AWS, Redshift and Glue, according to current job listings. Amazon pays data engineers above other Technology companies. Reviews put them at 3.5 on Glassdoor (toward the bottom of the pack) and negative sentiment on Blind. Amazon employees are stressed and employee happiness is trending down over the past year. Layoff risk scores moderate for the next 30 days. 101 data engineering roles are open right now.
Amazon
Technology · Seattle, LU · AMZN
live data · August 2, 2026
DE total comp
$237K median
L5 · senior level · $199K–$278K · 1319 verified datapoints
Hiring now
101 open DE roles
live from career pages
Team happiness
Stressed
employee happiness
Layoff risk (30d)
Moderate
Employee sentiment
3.5 / 5
NegativeEmployees
5,001–50,000
Amazon data engineer compensation
Each level's figure is the median of individual Amazon offers at that level, so it reflects a typical outcome rather than an average pulled up by a few large packages. Total comp counts base salary plus equity and bonus annualized over the vest, and the range shown is the middle half of offers, with the top and bottom quarters trimmed off.
What the Amazon signals mean
Sentiment tends to track team and org more than the company as a whole; the intensity, PIP reputation, and return-to-office posture weigh against the scale, mobility, and resume value of the AWS ecosystem.
Amazon employee sentiment, tracked weekly
Employee happiness for data engineers over the past year, so you can see which direction it is moving, not just where it sits today.
Recent Amazon events
Layoffs, leadership changes, and other major moves at the company, with dates.
- LayoffJul 2026~616 roles cut
- LayoffApr 2026~72 roles cut
- LayoffApr 2026~87 roles cut
- LayoffMar 2026~132 roles cut
- LayoffJan 2026~16,000 roles cut
- LayoffJan 2026~126 roles cut
- LayoffDec 2025~84 roles cut
- LayoffOct 2025~14,000 roles cut
- LayoffAug 2025~325 roles cut
Notable company events we track, with dates.
Amazon data engineering tech stack
The languages, storage, and processing tools Amazon data engineers actually work with, grouped by what they do. Tailor your system-design answers to this stack.
SQL
IcebergAmazon data engineer job openings
A live read on what they are hiring: open roles, recent postings, where, and at what level.
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.
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.
Amazon is looking for a motivated individual with strong database, analytical skills and technology experience to join the DIGI (Ad Sales Finance analytics ) team.
Design, develop, implement, test, document, and operate large-scale, high-volume, high-performance data structures for business intelligence analytics.
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.
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.
Build data pipelines purpose-built for LLM consumption and create data products and feature stores that serve GenAI applications in near real-time
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.
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.
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.
Practice for the Amazon loop
Round by round, the problems our model predicts for this company's interview. Rehearse the shapes their panels keep returning to.
What makes Amazon different
The things about this company that should shape every answer you give.
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.
Preparing for the Amazon loop
The round-by-round process, example questions, and prep plan are on the interview guide.
Amazon data engineer roles by level
Level-specific pages: the comp, the bar, and what the loop tests at each seniority.
Comp, level expectations, and role-specific prep.
Comp, level expectations, and role-specific prep.
Comp, level expectations, and role-specific prep.
Comp, level expectations, and role-specific prep.
Comp, level expectations, and role-specific prep.
Compare Amazon with other data engineering employers
How the role, pay, and loop stack up against peer companies.
Prepare at Amazon interview difficulty
- 01
Reading a solution is not the same as writing one
Every engineer who has frozen on a query they had read a dozen times knows the gap. The only preparation that closes it is producing the answer yourself, under time, before the interview does it for you
- 02
76% of hiring managers reject on the coding task, not the resume
From HackerRank's 2024 Developer Skills Report. Candidates who look strong on paper still fail the live screen if they haven't done timed, executable practice
- 03
5 problem shapes cover 80% of data engineer loops
Dedup, sessionization, top-N-per-group, slowly-changing dimensions, partition tricks. Writing the shapes by hand turns the unfamiliar into pattern recognition