Amazon Data Engineer Interview Guide

The process kicked off with an online assessment that tested SQL and data engineering fundamentals, which felt more like a screening to verify baseline knowledge than a filter. After that passed, I moved into four interview rounds, each with a distinct focus. The first phone round was straightforward — medium-level SQL questions, mostly around joins and window functions applied to Amazon business scenarios. Nothing too exotic, but they wanted to see how I'd approach optimizing queries and think through real data problems.

The technical rounds got progressively deeper. One session focused on Python, specifically data manipulation and wrangling — think pandas workflows and handling messy datasets. Another round was heavier on system design and data modeling, where they asked me to think through warehouse architecture, ETL pipeline design, and how I'd structure schemas for scale. They weren't looking for a perfect answer; they wanted to see my reasoning and how I'd trade off complexity, cost, and query performance. A Leetcode medium problem also came up — not Amazon-specific, just a standard coding challenge to verify I could write clean code under time pressure.

What surprised me most was how deliberately they tied everything back to Amazon's Leadership Principles. Every question, especially in the later rounds and the bar raiser round, had a behavioral undertone. They'd ask a technical question but were really evaluating how you think about customer obsession, ownership, and bias for action. The bar raiser round in particular felt like a deeper dive into data warehousing and big data concepts — they wanted someone who could own a data problem end-to-end. Manager and HR rounds rounded things out, but by that point it was more about fit and logistics. Overall the process felt streamlined and structured, though intensive. If you make it through all the rounds, they're clearly serious about you.