Ey Data Engineer Interview Guide

We’ve seen EY evaluate data engineers through a very practical lens, but not always a narrow one. Multiple candidates reported that the team cared less about abstract theory and more about whether you could handle the realities of pipelines, reporting, and production data work. That shows up in the recurring emphasis on PySpark internals, SQL windowing, joins, and nested aggregations, along with questions tied directly to past projects. In the stronger experiences, the interviewers pushed for specifics on how data would actually move and transform, not just what the tools do in isolation.

A recurring theme is that EY can be surprisingly architecture-heavy for this title. One candidate was asked to write Dataflow pipeline code and discuss GCP services, while another was pulled into GRC implementation, access control, and even finance-related topics from a security manager. That mismatch is important: our candidates report that EY sometimes tests for adjacent domain knowledge, especially around governance and enterprise controls, so the safest assumption is that the bar extends beyond pure coding. The people who seemed most aligned were the ones who could connect their work to design decisions, data handling choices, and business context without sounding rehearsed.

We also see a clear split in how technical the process can feel. Some candidates described a fast, straightforward conversation, while others faced a timed SQL screen that felt like a speed test under pressure. The common denominator is that EY wants candidates who can explain their reasoning cleanly and stay grounded in real implementation details. If your background is strong but narrowly focused, the risk here is not lack of talent — it’s being surprised by how often the conversation leaves the job description behind.