Went through the EY data engineer interview loop last month for a role in their Technology Consulting practice. Sharing notes because I couldn't find much specific info before I went in.
Total rounds: four. Phone screen with a recruiter (standard 20 min, background + availability), then a technical screen with someone from the data engineering team, then two final-round interviews on the same day (one technical, one behavioral/case-mix).
The SQL was real. Not "write a SELECT" SQL. More like: given this messy schema, write a query that handles NULLs correctly, uses window functions to get running totals, and explain why your approach is better than a subquery. One question gave me a slowly changing dimension scenario and asked how I'd handle it in a query before diving into the architecture side. Intermediate-to-advanced range. Know PARTITION BY, LEAD/LAG, RANK vs DENSE_RANK.
Pipeline questions were more design-oriented. They asked me to walk through how I'd build an ELT pipeline for a client that's ingesting data from 10+ source systems with different schemas. I talked about orchestration (they seemed fine with Airflow references), idempotency, schema evolution, monitoring. No right answer, they wanted to see that I thought about failure modes.
They also asked about Spark. Not deep Spark tuning, more like: do you know the mental model, have you used it in anger. I had real experience so it went fine, but I wouldn't fake it.
Behavioral side was fairly standard EY stuff: client impact, dealing with ambiguity, a time you had to simplify technical concepts for a non-technical stakeholder. They care a lot about that last one because the work is consulting-adjacent and you will be explaining data pipelines to clients who don't know what a DAG is.
Timeline was about 3 weeks start to finish. Offer came ~5 business days after the final round.
Happy to answer specifics if anyone's in the same loop.