EY Data Analyst interviews reported here range from screening and assessments to SQL, Power BI, case-style, and behavioral discussions. Prepare to explain your own work as clearly as your technical choices.
$94K
Avg. Base Comp
$113K
Avg. Total Comp
2 rounds
Typical Rounds
3-5 weeks
Process Length
EY Data Analyst candidates describe a broad process rather than one fixed sequence. One reported route included an online assessment focused on verbal reasoning, interpretation, adaptability, and written accuracy, followed by a recorded video response with limited preparation time. Another candidate encountered a broad multiple-choice assessment spanning Python, SQL, behavioral judgment, prior experience, and EY policies. The recurring theme is that candidates may need to connect technical knowledge to their own responsibilities, not simply recite definitions.
Technical conversations varied by background and interviewer. Reported SQL coverage includes joins, grouping, ordering, window functions, basic DBMS concepts, and query optimization. Power BI-focused interviews have covered projects, filter types, relationships, and function performance; a related intern account also described detailed questions about Power Query, data modeling, DAX, and report optimization. Case studies, Excel tests, guesstimates, and questions about value delivered in previous projects also appear in the reports.
Prepare a clear walkthrough of a project: the business question, data work, choices made, result, and your contribution. Pair that with concise answers for strengths, weaknesses, career plans, motivation for EY, and situations where you adapted or handled a challenge. The available reports are limited and show meaningful variation, so treat these as possible components rather than a universal script.
Synthesized from 6 candidate reports by our editorial team.
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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 | |
| Size of Joins | |
| Sort Strings | |
| Xgboost vs Random Forest | |
| Forecasting New Year Revenue | |
| Hurdles In Data Projects | |
| Duplicate Rows | |
| Classification and Regression | |
| Overfit Avoidance | |
| Pipeline Transformation Failures | |
| Company Acquisition Choice | |
| Marketing Workflow Optimization | |
| Stakeholder Communication | |
| Simple Explanations | |
| Relational Migration | |
| Why Do You Want to Work With Us | |
| Your Strengths and Weaknesses | |
| Data Cleaning Experiences | |
| Marketing Dollar Efficiency | |
| Linear vs Logistic Regression | |
| Backpropagation Explanation | |
| Analyzing Multiple Data Sources | |
| Rolling Bank Transactions | |
| Closest SAT Scores | |
| Experiment Validity | |
| Raining in Seattle | |
| Maximum Profit | |
| Bagging vs Boosting |
Synthesized from candidate reports. Individual experiences may vary.
Candidates report online assessments that may emphasize verbal reasoning, interpretation, adaptability, and written accuracy. A recorded-video stage was also reported, with a short preparation window before a final response about motivation for the EY role. Another account described a broader multiple-choice assessment covering Python, SQL, behavioral judgment, work history, and EY policies.
Candidates report SQL questions on joins, GROUP BY, ORDER BY, window functions, DBMS fundamentals, and query optimization. Some interviews also included a case study, guesstimates, or an Excel test. Be ready to explain your reasoning and describe the value you delivered in prior projects, since those themes were explicitly discussed.
A reported second interview focused on Power BI after an initial SQL round, including filter types, relationships, and function performance. Related EY analyst-intern evidence describes project walkthroughs, Power Query transformations, data-model choices, DAX, and reporting optimization. Depth may depend on the experience presented on your resume.
Candidates report discussions of strengths, weaknesses, five-year plans, motivations, projects, responsibilities, and how they approach a business question. Prepare specific examples that show how you work with data and communicate a result to stakeholders, while keeping answers grounded in work you personally performed.
