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.
$108K
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.
Synthesized from 1 candidate report by our editorial team.
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Real interview reports from people who went through the Genpact process.
The interview was pretty focused on BI work rather than broad data science, and the hardest part for me was how much they expected me to switch between SQL, Power BI, and a bit of Python in the same conversation. I had two technical rounds centered on SQL and Power BI, and then an initial technical round that also covered Python basics, my past projects, and a case study tied to their current hiring need. In the Python part, they kept it simple with strings, arrays, loops, and fundamentals, but they spent much more time digging into my end-to-end AI/ML projects and asking how I’d apply them to their environment. They also asked whether I was ready to build simulation environments, forecasting solutions, and automation around ML project scopes, so it felt very practical and business-facing.
On the SQL side, the questions were more applied than theoretical. I was asked to solve a top-3-sales-every-month problem, and they specifically wanted to see window functions and derived queries. The Power BI discussion was fairly detailed too: optimization techniques, the difference between ALL and ALLSELECTED, RELATED, FILTER, and REMOVEFILTER, plus broader topics like visualizations, charts and their purpose, schedules, incremental refresh, RLS, and BI workspace features. It wasn’t a super deep coding interview, but it did require being comfortable explaining why you’d use a certain DAX function or Power BI feature in a real reporting setup. Overall the process felt relevant to the role and pretty hands-on. I ended up getting the offer, and my main takeaway is to prepare for practical SQL patterns and be ready to talk through Power BI behavior and your own project experience in a very applied way.
Prep tip from this candidate
Practice SQL window-function problems like top-N per group, and make sure you can explain when to use ALL vs ALLSELECTED, RELATED, FILTER, and REMOVEFILTER in Power BI. Also be ready to discuss your AI/ML projects end to end and how they could translate into forecasting or automation work.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Genpact
Create top_ads with the top 3 ads and return the row counts for inner, left, right, and cross joins with ads
| Question | |
|---|---|
| Hurdles In Data Projects | |
| Cumulative Sales By Product | |
| Count Transactions | |
| Sort Strings | |
| Assumptions of Linear Regression | |
| Digit Accumulator | |
| Multicollinearity in Regression | |
| Measuring Customer Service Quality | |
| Branch Sales Pivot | |
| Extra Delivery Pay | |
| loc vs iloc | |
| 2nd Highest Salary | |
| Top Three Salaries | |
| Rolling Bank Transactions | |
| Closest SAT Scores | |
| Experiment Validity | |
| Top 3 Users | |
| Merge Sorted Lists | |
| Find the Missing Number | |
| Retailer Data Warehouse | |
| Employee Salaries | |
| Manager Team Sizes | |
| Month Over Month | |
| Longest Streak Users | |
| Largest Salary by Department | |
| First Touch Attribution | |
| Prime to N | |
| Top 5 Turnover Risk | |
| SELECTive Wine Connoisseur |
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.
