Ey Data Analyst Interview Guide

The part that stood out most was how quickly the interview moved from general conversation into practical data work. I opened with the usual “Tell me about yourself” prompt and then walked through my analytical process, including how I approach a business question, work with the data, and turn the result into something useful for stakeholders. From there, the interviewer tested my SQL knowledge by asking me to explain joins and the differences between the various join types. We also discussed dashboard design, especially how I balance visual appeal with functionality and decide which chart best communicates a particular result.

Earlier in the process, I completed an online assessment focused more on verbal reasoning than technical analytics. It tested comprehension, interpretation, adaptability, reasoning, and attention to correct spelling, punctuation, and grammar. There was also a recorded-video stage. I had about five minutes to prepare before recording answers to questions such as why I wanted the position at EY. A practice option was available, but the actual response could not be redone once submitted, which added some pressure.

The more technical discussion could extend beyond basic reporting concepts. I encountered detailed questions about data-pipeline architecture and ETL processes, along with distributed systems such as Hadoop and Spark. I was also asked how I would optimize SQL queries and design scalable data workflows. These questions were challenging because they tested both conceptual understanding and the ability to explain practical design decisions, rather than just recalling definitions.

I ultimately received and accepted an offer. My main takeaway is to prepare for a broad mix: precise verbal reasoning, concise recorded answers, SQL fundamentals, dashboard judgment, and potentially deeper data-engineering concepts. For the video portion, use the practice opportunity seriously because the submitted recording is final.