Candidates describe Deloitte Data Analyst interviews as SQL-heavy, with resume project walk-throughs and business cases, and timelines that run from about a week to over three months.
$108K
Avg. Base Comp
$115K
Avg. Total Comp
3-4 rounds
Typical Rounds
1-13 weeks
Process Length
Deloitte Data Analyst candidates describe a practical, mostly SQL-driven loop, though the exact steps change from office to office and team to team. The technical conversation most often centers on SQL joins, window functions and ranking. Reported questions include inner versus outer joins, finding the second highest salary, ranking records, and calculating the repeat rate of orders. One candidate said the interviewers cared as much about what the result meant for the business as about whether the query ran.
Interviewers also want to hear about your own work. Several candidates were asked to walk through resume projects, explain the challenges they hit, and describe how they pulled insight from large datasets. Others were asked how they would clean messy data, how they would check that data can be trusted, and how they would diagnose a drop in product sales. Excel lookups, Python or pandas basics, and Power BI each came up in individual reports.
Case work shows up in different forms: a business case alongside SQL, a consulting-style case study with a director, or a scenario laid out in a video round. The mix is not uniform. One candidate found the video and IQ-style items had little to do with analytics, and another was opened with a broad question about what success means to them.
Timelines vary widely. Several candidates report about a week to two weeks from first contact to offer, one describes about a month across manager, senior manager and director interviews, and one reports a wait of more than three months across three rounds. Practice explaining your SQL reasoning out loud, rehearse two or three project stories, and keep a simple framework ready for unfamiliar business problems.
Synthesized from 20 candidate reports by our editorial team.
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Real interview reports from people who went through the Deloitte process.
I went into the Deloitte Data Analyst interview expecting a fairly standard conversation about analytics work. The panel started late, but once we got going the discussion flowed well, and I left feeling good about how I'd answered. The questions were a mix of IQ-style items and behavioral scenarios. None of them were especially hard, but very few had much to do with data analytics, so the whole thing felt more general than I'd prepared for.
The video portion stood out the most. It used scenario-based questions built around a specific situation they lay out for you, which made it hard to prepare in a targeted way. You can't really rehearse for a scenario you haven't seen yet. There were also not many motivational questions, so I didn't get much room to talk about why I wanted this particular role.
I never received feedback after the interview, which was frustrating given how much time I put into preparing. I later found out the role went to someone with noticeably less relevant experience, apparently based on criteria that were never listed in the job requirements. I'm still not sure what to make of that, but it left me doubting how transparent the process was. If I had to do it again, I'd spend my prep time getting comfortable working with data rather than memorizing general interview answers.
Prep tip from this candidate
The video round uses a specific scenario they give you, so you can't drill exact questions; instead, practice quickly working through unfamiliar data scenarios and talking through your reasoning. Expect the IQ-style and behavioral items to be less tied to analytics than you might assume, so prepare for those separately.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Deloitte
Select the 2nd highest salary in the engineering department
| Question | |
|---|---|
| Raining in Seattle | |
| Size of Joins | |
| Maximum Profit | |
| Duplicate Rows | |
| Bagging vs Boosting | |
| Assumptions of Linear Regression | |
| Youtube Recommendations | |
| Normalize Grades | |
| Using R Squared | |
| Justify a Neural Network | |
| Missing Housing Data | |
| Find Duplicate Numbers in a List | |
| Relational Migration | |
| Swap Variables | |
| Hurdles In Data Projects | |
| FAQ Matching | |
| Classification and Regression | |
| Slow SQL Query | |
| Data Pipelines and Aggregation | |
| Yelp-like System | |
| Bias vs. Variance Tradeoff | |
| User Event Data Pipeline | |
| Client Solution Pushback | |
| Data Preparation for Imbalanced Data | |
| Multicollinearity in Regression | |
| String Palindromes | |
| Seller Type Modeling | |
| Open Source Reporting Pipeline | |
| Blob Indexing |
Synthesized from candidate reports. Individual experiences may vary.
Candidates describe a short recruiter or HR screening call about background and motivation, one lasting about ten minutes. Some also report an online assessment: aptitude and logical reasoning in one account, data structures and algorithms in another, and a written test in a third. One candidate was asked to give scenario-based answers in a video round. Steps differ by team, so follow the instructions you are sent.
SQL is the most reported topic: inner versus left or outer joins, group by and having, window functions and ranking, second highest salary, and order repeat rate. Individual candidates also mention Index Match in Excel, Python and pandas basics, regression assumptions, and data cleaning. Several were asked to walk through resume projects in detail, including challenges faced and insights found.
Candidates report cases such as diagnosing a quarterly drop in sales, reasoning about a business metric alongside SQL, calculating truck CO2 emissions in a data model, and explaining how they would verify that data is trustworthy. Reports suggest interviewers look for a structured approach and an explanation of what the result means for the business, not just a working query.
Later conversations are with a manager, senior manager, director or hiring manager, depending on the account. They cover communication, teamwork and culture fit, and sometimes more technical or case content. One candidate received an offer a week after the final round and could negotiate terms, and another had an HR call about compensation. Feedback is inconsistent, and one candidate never received any.
