EY Data Engineer candidates report SQL-heavy technical rounds, PySpark and pipeline scenarios, project walkthroughs, and a managerial or HR conversation. The number and order of rounds vary across reports.
$115K
Avg. Base Comp
$150K
Avg. Total Comp
Not reported
Typical Rounds
Not reported
Process Length
Candidates who describe EY Data Engineer interviews point to the same core skills: SQL under time pressure, PySpark fundamentals, and clear explanations of your own projects. The format differs from person to person, so prepare for a range rather than one fixed script.
One candidate describes an online SQL multiple-choice test with 20 questions at three minutes each, built around multi-table joins and nested aggregations. Later rounds in that account covered triggers, stored procedures, normalization and resume-based scenarios. Another reports two technical rounds and an HR round. The first covered PySpark partitioning and bucketing, and the second leaned on SQL window functions. A third account describes three rounds: background and SQL/Python, a harder technical round, then a managerial and behavioral conversation. Questions there included second-highest salary, duplicate records, consecutive-activity problems, and reversing words or finding the first non-repeating character in Python.
Cloud and orchestration depth also appears. One candidate was asked to write Dataflow pipeline code and discuss GCP services. Another was asked about BigQuery, Dataproc, Airflow, schema evolution and Spark internals such as the Catalyst Optimizer.
Practice window functions, joins and de-duplication aloud, and have a concise project story ready, including your tradeoffs. Ask about next steps at the end of each round so you know which format comes next.
Synthesized from 10 candidate reports by our editorial team.
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Real interview reports from people who went through the Ey process.
The process began with a 15–20 minute technical call about my experience and fundamentals. I then completed an L1 technical interview on Microsoft Teams that lasted about 45 minutes.
The L1 round went deeper into my data engineering experience, especially GCP, BigQuery, PySpark, SQL, Airflow, and scenario-based questions. I was most comfortable discussing projects I had worked on and explaining why I chose particular approaches. The harder part was moving from direct technical questions to real-time production scenarios involving troubleshooting or design.
Questions included explaining a data engineering project architecture; ingesting data into GCP; PySpark transformations with Dataproc; the Catalyst Optimizer and Tungsten Engine; Spark DAG stages; surrogate versus primary keys; duplicate removal in SQL; schema evolution; audit and control tables; passing data between Airflow DAGs; BigQuery slots and authorized views; materialized-view SQL; and project business logic and transformations.
At the time of writing, I had completed the initial screen and L1 technical round and was waiting for the next step.
Prep tip from this candidate
Prepare a concise walkthrough of your data engineering projects and the tradeoffs behind them. Review GCP, BigQuery, PySpark, SQL, Airflow, and production troubleshooting scenarios.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Ey
Select the 2nd highest salary in the engineering department
| Question | |
|---|---|
| Top 3 Users | |
| Longest Streak Users | |
| Find the First Non-Repeating Character in a String | |
| Size of Joins | |
| Resumable Fact Table Load | |
| Skyscanner Partner ETL | |
| Sort Strings | |
| Xgboost vs Random Forest | |
| Hurdles In Data Projects | |
| Real-Time Hashtag Partitioning | |
| Duplicate Rows | |
| Classification and Regression | |
| Pipeline Transformation Failures | |
| Mouse Search | |
| Azure Kubernetes Infrastructure | |
| Simple Explanations | |
| Relational Migration | |
| Why Do You Want to Work With Us | |
| Marketing Workflow Optimization | |
| Processing Large CSV | |
| Your Strengths and Weaknesses | |
| Stakeholder Communication | |
| Data Cleaning Experiences | |
| Linear vs Logistic Regression | |
| Backpropagation Explanation | |
| Analyzing Multiple Data Sources | |
| Rolling Bank Transactions | |
| Closest SAT Scores | |
| Merge Sorted Lists |
Synthesized from candidate reports. Individual experiences may vary.
Candidates report an initial call covering their current role, skills, and expectations. One candidate describes a short technical call at this stage instead. Prepare a short project summary and a clear reason for changing jobs.
One candidate reports a timed SQL multiple-choice test with 20 questions at three minutes each, focused on joins, nested queries and aggregations. This step may not apply to every applicant, so treat it as possible rather than guaranteed.
Candidates report one to two technical rounds covering window functions, CTEs, duplicate handling, PySpark partitioning and bucketing, Python string problems, GCP or BigQuery, Airflow, and pipeline scenarios. Expect to explain your reasoning aloud and connect your answers to production troubleshooting.
Some candidates describe resume-based and scenario-based discussion of past projects. Others describe architecture topics such as writing Dataflow pipeline code, ingesting data into GCP and explaining a project's architecture. The depth may vary, so be ready to defend every line on your CV.
Reports include a managerial and behavioral round on strengths, teamwork and pressure, and a standard HR round. Outcomes among candidates ranged from an offer to rejections and waiting for a reply.
