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50 of 50 companies
Difficulty ~3 / 5
Difficulty ~3.5 / 5
Difficulty ~2.8 / 5
Difficulty ~3.4 / 5
Difficulty ~2.6 / 5
Press a bar to filter the list above.
Each bar counts accounts, by keyword. "Tested" looks at a company's round names, round details and prep priorities. "Rejected for" looks at the reasons given for rejecting candidates. It shows how many accounts mention a topic, not what share of candidates meet it. Some accounts are composites, and each company page says which.
Pick your years of experience.
Whether the basics are solid and you can explain them out loud.
Prep guides and candidate reports, not hiring-company data. Guides agree on SQL, Python and star schemas. They differ on how much pipeline design to expect.
The SQL, Python and modeling rounds. An unstated grain is the most-cited modeling mistake.
Built from the 50 company accounts on this page and from prep guides. This is the level with the most reports.
Design and modeling, plus an ownership round. Your level is set by how you lead a design.
Few public sources are about 4–8 years in particular. Most describe "senior" in general, and the company reports are single accounts. Treat this as a good guide to the shape of the loop, not a fixed list.
Scope and judgment. How far your work reaches, and whether you can move a decision across teams you do not manage.
Public reports for Staff data engineers are scarce. Much of what exists is from prep blogs, and some describes Staff software loops. The themes agree across sources, but the round names differ by company.
What each one tests, and the chapter for it.
The first filter: motivation, level fit, and a quick read on whether your projects match the role before any code is written.
The most common technical filter for data engineers — window functions and analytical SQL, plus practical Python/PySpark under time pressure.
Designing batch and streaming data pipelines end to end: ingestion, storage, processing, serving, and the failure paths in between.
Build or debug a small pipeline on your own time — judged on correctness, tests, documentation, and the decisions you can defend.
A bank of nine true stories, told in STAR with a number at the end, mapped to the questions interviewers actually ask and to Amazon’s Leadership Principles.
The final stretch: hiring-manager and leadership conversations, recovering from a weak answer, and handling the offer.