Recent KPMG Data Analyst reports describe a variable two- to three-round process that emphasizes resume depth, motivation, practical project discussion, and foundational analytics skills.
$94K
Avg. Base Comp
$102K
Avg. Total Comp
2-3 rounds
Typical Rounds
2-3 weeks
Process Length
The clearest KPMG Data Analyst reports describe interviews that combine resume discussion, motivation, practical analytical knowledge, and situational judgment. Prepare to explain your resume and your direct contribution to past work clearly. Candidates were asked to walk through their experience, college background, career goals, and reasons for wanting to join KPMG. Know the details of each example on your resume so that you can explain what you did without relying on vague team-level descriptions.
Technical depth varied across the data-focused accounts. Reported topics included SQL, basic statistics, p-value interpretation, database-style reasoning, and Power BI row-level security. One candidate described a murder-mystery-style database question that emphasized the approach to the problem, while another was asked about practical experience with row-level security. Review the concepts you have genuinely used and practice explaining how you would investigate a problem, make a decision, and communicate your reasoning.
Behavioral and situational questions were also part of the interviews. Candidates discussed leadership, teammate conflict, career direction, and why they wanted the role or team. Some interviews combined these topics with technical questions rather than separating them into distinct stages. Prepare a few concise examples that show your judgment and communication, and expect interviewers to probe the choices you made. Reported formats varied, so use these topics as preparation priorities rather than assuming one fixed sequence.
Synthesized from 14 candidate reports by our editorial team.
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Real interview reports from people who went through the Kpmg process.
What stood out to me most was how much of the process was designed to filter for communication and fit before they really dug into technical ability. I had four rounds overall: an aptitude round, a group discussion, an assessment, and then a face-to-face HR interview. The group discussion was very much about participation and confidence, not just having the right answer. The topic I got was the effect of social media on the real world, and it was clear that they wanted to see whether I would actively contribute instead of just sitting back. That was the first big gate.
After that came the assessment and then the HR conversation, which felt more like a structured fit check than a deep technical interview. They asked me to describe myself and explain how I fit the job, along with questions about my studies, work experience, and why I was interested in the role. In the technical part, the most important question was scenario-based and centered on valuations, so they were testing whether I could think through a business case rather than just define terms. The whole process moved fairly quickly, and they were responsive after the application, but it was still pretty selective. My impression was that KPMG cared a lot about how you present yourself, how you handle discussion, and whether you can connect your background to the role. I didn’t get an offer in the end, so I’d say the safest prep is to practice speaking clearly in a group setting, be ready to explain your background and motivation, and review valuation scenarios so you can talk through them confidently.
Prep tip from this candidate
Practice speaking up early in a group discussion, because active participation was treated as a screening criterion. Also prepare a clear explanation of your background and a scenario-based valuation answer, since that was the main technical prompt.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Kpmg
Select the 2nd highest salary in the engineering department
| Question | |
|---|---|
| Rolling Bank Transactions | |
| Longest Streak Users | |
| P-value to a Layman | |
| Fair Coin | |
| Hurdles In Data Projects | |
| Spam Classifier | |
| Testing Price Increase | |
| Assumptions of Linear Regression | |
| Modifying a Billion Rows | |
| Data Preparation for Imbalanced Data | |
| Algorithm Reliability | |
| Multicollinearity in Regression | |
| Scalable Data Pipelines | |
| Testing Constraints | |
| Increase Search Ads | |
| Client Solution Pushback | |
| Why Do You Want to Work With Us | |
| Your Strengths and Weaknesses | |
| Branch Sales Pivot | |
| Google Docs Drop | |
| Late Orders | |
| Statistically Significant Test | |
| Delivery Online | |
| Closest SAT Scores | |
| Experiment Validity | |
| Raining in Seattle | |
| Size of Joins | |
| Maximum Profit | |
| Bagging vs Boosting |
Synthesized from candidate reports. Individual experiences may vary.
Reported processes may begin with an HR conversation or an online aptitude and logical-reasoning screen. Candidates were asked about their background, interest in KPMG, career direction, and motivation for the role. One report described this as the first of three interviews; another described it as an online baseline screen before technical discussion.
Data-focused reports describe SQL questions, basic Python and data-structures-and-algorithms questions, p-value interpretation, database reasoning, and discussion of past projects. Candidates may be asked to explain a substantial project challenge and how they handled it. One report also included a situational question about Power BI row-level security.
Candidates reported later conversations with managers, senior associates, or senior management. These discussions can combine role-related technical questions with situational and behavioral prompts, including conflict, pressure, and career goals. One candidate reported two 30-minute interviews with senior management, while other reports described a final HR round.
