Candidates describe Gartner analyst interviews as a recruiter screen, SQL and business-scenario rounds, and a hiring manager, case or panel conversation. Reported processes run four to six rounds.
$77K
Avg. Base Comp
$85K
Avg. Total Comp
4-6 rounds
Typical Rounds
3-8 weeks
Process Length
Candidates describe the Gartner Data Analyst interview as a mix of fundamentals and business judgment. One candidate reported four rounds over about three weeks: a recruiter call, two one-hour technical interviews and a 45-minute hiring manager conversation. That candidate was asked for a top three products by revenue within each category query, which points to joins, aggregations and window functions. Another candidate was asked for the 3rd highest salary, so ranking queries are worth drilling.
The business-scenario side matters as much as the SQL. One candidate had to define metrics, analyze and communicate findings for a declining user-engagement problem. The same candidate was asked about validating data, investigating a sudden metric drop, A/B testing, dashboards and Python. A different candidate reported Power BI questions, including row-level security, plus SQL and data warehousing fundamentals with a hiring manager and a skip-level interviewer.
Earlier screens leaned on experience matching. Reported questions included why you want Gartner, how your projects map to the role, and a self-rating of 1 to 10 on Python, SQL, R, Excel and Tableau. One screen also covered regression versus correlation and a few puzzles. One candidate described a longer process of about two months with a case study and a presentation panel. Outcomes were mixed, including offers, several rejections and one role that was reportedly made redundant after the final panel. Have concrete project stories and a structured way to explain your analysis to non-technical stakeholders.
Synthesized from 7 candidate reports by our editorial team.
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Topics based on recent interview experiences.
Featured question at Gartner
Select the 2nd highest salary in the engineering department
| Question | |
|---|---|
| Bagging vs Boosting | |
| Justify a Neural Network | |
| Forecasting New Year Revenue | |
| Variate Anomalies | |
| Random Forest from Scratch | |
| Why Do You Want to Work With Us | |
| Linear vs Logistic Regression | |
| Top Three Salaries | |
| Longest Streak Users | |
| Rolling Bank Transactions | |
| Closest SAT Scores | |
| Employee Salaries | |
| Largest Salary by Department | |
| Top 3 Users | |
| First to Six | |
| Prime to N | |
| Find the Missing Number | |
| Size of Joins | |
| Fair Coin | |
| Experiment Validity | |
| Paired Products | |
| Get Top N Frequent Words | |
| First Touch Attribution | |
| Raining in Seattle | |
| Assumptions of Linear Regression | |
| 500 Cards | |
| Month Over Month | |
| Swipe Precision | |
| Duplicate Rows |
Synthesized from candidate reports. Individual experiences may vary.
Candidates report an initial recruiter or phone screen covering background, prior projects, motivation for Gartner, and sometimes work authorization and salary expectations. One screen asked for 1-10 self-ratings on tools such as Python, SQL, Excel and Tableau, with examples. Prepare specific project stories and a clear reason for wanting the role.
Candidates report SQL questions such as top three products by revenue per category and the 3rd highest salary, along with data validation and troubleshooting discrepancies. One candidate also faced data warehousing fundamentals and resume project deep dives. Expect joins, aggregations and window functions to be useful, though the exact format may differ by team.
Reported second-stage rounds were more applied. One candidate worked through a declining engagement scenario, A/B testing and a metric drop. Another received Power BI and row-level security questions from a skip-level interviewer. Others describe a written exercise or a case study. Practice explaining metric definitions, analysis steps and stakeholder communication.
Final stages varied by candidate. One reported a 45-minute hiring manager behavioral round on cross-functional work, ambiguity and incomplete data. Others described a panel or presentation of their case findings, or an HR discussion about salary. Prepare STAR-style examples and a concise way to present findings to a mixed audience.
