100 AWS Data Engineer Interview Questions & Answers

The complete senior/lead prep guide for cloud-native batch, streaming, and warehousing roles

The part nobody warns you about

The hardest question in a senior AWS data engineering interview is rarely “what is Kinesis?”

It’s the pause after: “Now design the same pipeline in real time — and make sure it still reconciles with your nightly batch.”

That silence is the whole interview. The interviewer is not checking whether you can name a Shard. They’re watching how your mind moves through trade-offs — latency against cost, serverless against control, exactly-once against operational sanity, “clever” against “the on-call engineer can fix it at 2 a.m.”

I’ve sat through enough of these loops, and read enough of them, to know the pattern: the candidates who get offers are not the ones who memorized the most services. They’re the ones who reason out loud, quantify their claims, and know why one design beats another — not just that it does.

So I wrote the guide I wish someone had handed me.

Below are 100 questions and answers covering the entire surface of a modern AWS data engineering role: S3 and the data lake, Glue and ETL, Redshift and warehousing, EMR and Hadoop, real-time streaming, PySpark internals, SQL tuning, orchestration, NoSQL, and the security/DataOps layer that ties it all together.