Genpact Business Intelligence interview typically runs 3 rounds: an initial technical round, a SQL round, and a Power BI round. The process took about a week and was practical, hands-on, and business-facing.
$74K
Avg. Base Comp
$155K
Avg. Total Comp
3
Typical Rounds
2-4 weeks
Process Length
We've seen Genpact lean hard into hands-on BI judgment rather than abstract data theory. In the candidate experience we have here, the conversation kept moving across SQL, Power BI, and a little Python, but the real signal was whether the candidate could connect those tools to a business reporting problem. That meant explaining why a DAX function like ALLSELECTED or REMOVEFILTER would be used in a real dashboard, not just naming it correctly. The same pattern showed up in the SQL work: the top-3-sales-every-month problem was less about trivia and more about whether the candidate could structure a solution with window functions and derived queries under realistic constraints.
A recurring theme is that Genpact seems to care deeply about applied project experience. The interviewer spent more time probing end-to-end AI/ML projects, forecasting ideas, and automation use cases than on Python syntax itself, and the Python questions stayed at the fundamentals level. That tells us the bar is not “can you code in isolation,” but “can you translate technical work into something useful for their environment.” Candidates who do well here usually sound comfortable discussing reporting tradeoffs, BI workspace features, incremental refresh, and RLS as operational choices. In other words, the strongest signal is practical fluency: knowing how the pieces fit together in a live business setup.
Synthetized from 1 candidates reports by our editorial team.
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Topics based on recent interview experiences.
Featured question at Genpact
Compute the cumulative sales for each product.
| Question | |
|---|---|
| Assumptions of Linear Regression | |
| Digit Accumulator | |
| Count Transactions | |
| Multicollinearity in Regression | |
| Extra Delivery Pay | |
| loc vs iloc | |
| 2nd Highest Salary | |
| Top Three Salaries | |
| Rolling Bank Transactions | |
| Closest SAT Scores | |
| Employee Salaries | |
| Manager Team Sizes | |
| Find the Missing Number | |
| Merge Sorted Lists | |
| Hurdles In Data Projects | |
| First Touch Attribution | |
| Largest Salary by Department | |
| Top 3 Users | |
| Experiment Validity | |
| Retailer Data Warehouse | |
| Prime to N | |
| SELECTive Wine Connoisseur | |
| Bagging vs Boosting | |
| Google Maps Improvement | |
| Top 5 Turnover Risk | |
| Over-Budget Projects | |
| P-value to a Layman | |
| Size of Joins | |
| Swipe Precision |
Synthesized from candidate reports. Individual experiences may vary.
The first round combined Python basics, past project discussion, and a case study aligned to Genpact’s current hiring need. Expect practical questions on strings, arrays, loops, and fundamentals, along with deeper probing into end-to-end AI/ML projects and how they could be applied in a business environment.
This round focused on applied SQL problem solving rather than theory. Candidates were asked to solve business-style queries such as finding the top 3 sales each month, with emphasis on window functions and derived queries.
The final technical discussion centered on Power BI depth and practical reporting knowledge. Topics included DAX functions like ALL, ALLSELECTED, RELATED, FILTER, and REMOVEFILTER, as well as optimization, visualizations, incremental refresh, RLS, schedules, and workspace features.