Infosys Data Analyst interviews often combine an aptitude round or online assessment, a resume-driven technical interview centered on SQL (with Power BI for some candidates), and behavioral questions.
$89K
Avg. Base Comp
$107K
Avg. Total Comp
3 rounds
Typical Rounds
Not reported
Process Length
Infosys Data Analyst candidates describe a process that often opens with an aptitude round or an online assessment before any live interview. One candidate took a proctored test with three moderate-level problems in two hours, and another report mentions a 45-minute coding test ahead of an interview of about an hour. SQL is the most consistent technical theme across reports, mostly at a fundamentals level: joins, writing a query for the second-highest salary, and selecting employees whose salary is above the average.
The technical interview is frequently resume-driven. Interviewers spend real time asking candidates to walk through their own projects and prior work, so know every line on your resume. One candidate was asked how an analytics app from their resume would need to change its schema or architecture to handle roughly ten times more dashboard requests. Power BI can come up in depth. One candidate was asked about Desktop versus Service, import, direct, and live connections, gateways and their types, sharing a report outside the organization, and which custom visual they had used. Tableau is mentioned as a possible topic in another report. Some candidates also saw basic OOPs and DBMS concepts, simple Python problems using arrays and dictionaries, or high-level machine learning questions such as bagging versus boosting, regression algorithms, or LSTM.
Behavioral questions cover your introduction, strengths and weaknesses, why you want to join Infosys, career goals, and your exact contribution on a team. Candidates repeatedly say that explaining basics clearly and tying them to real project work mattered more than solving anything complex. Reported structures range from a single conversational round to three stages, and one candidate noted that updates between rounds could be slow.
Synthesized from 23 candidate reports by our editorial team.
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Real interview reports from people who went through the Infosys process.
The interview ran about 30-45 minutes and covered a mix of Python, SQL, and behavioral questions. They started with theoretical topics and quickly moved into DSA problems — I got two Python coding questions to solve, including a dynamic programming problem that felt like the core technical challenge. One was around finding patterns or optimal solutions, which required careful thinking about edge cases and time complexity.
After the coding portion, they gave me a SQL query to write. This wasn't just a one-off — they were clearly testing how I could optimize and refine my queries. They asked follow-up questions like how I'd improve what I'd written, so knowing your solution isn't final and being ready to iterate was important. They also drilled into how I'd handle data quality issues in practice, like null values or incorrect data, which felt more about practical thinking than textbook answers.
The interviewer leaned heavily on my resume. I had mentioned LLMs in my background, so they asked me to walk through a user flow and system design for an LLM-based project — essentially thinking out loud about how a system would work end-to-end. It was conversational rather than a formal system design session, but it showed they wanted to understand how I think through problems, not just memorize solutions. The whole vibe was testing both technical execution and reasoning ability.
Prep tip from this candidate
Brush up on dynamic programming problems and standard SQL query optimization — be ready to refine your queries and explain why you made certain choices. If you mention any projects involving LLMs or complex systems on your resume, prepare to sketch out user flows and explain how the components interact.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Infosys
Select the 2nd highest salary in the engineering department
| Question | |
|---|---|
| Top Three Salaries | |
| Top 3 Users | |
| Find the Missing Number | |
| Bagging vs Boosting | |
| Booking Regression | |
| Size of Joins | |
| P-value to a Layman | |
| Normalize Grades | |
| Covariance vs Correlation | |
| Retailer Data Warehouse | |
| Find Duplicate Numbers in a List | |
| Hurdles In Data Projects | |
| Digitizing Student Test Scores | |
| RAG Strict Source Control | |
| Classification and Regression | |
| Merchant Dashboard Design | |
| Concurrent LLM Serving | |
| Swap Variables | |
| Ticket Agent Analysis | |
| Check Matching Parentheses | |
| The Longest Journey | |
| Seller Type Modeling | |
| String Palindromes | |
| Addressing Data Quality Issues | |
| Blob Indexing | |
| Pipeline Transformation Failures | |
| Safe Deployments | |
| Scalable Data Pipelines | |
| Testing Constraints |
Synthesized from candidate reports. Individual experiences may vary.
Some candidates begin with an aptitude round, in one case held at their college. One candidate instead took an online proctored technical assessment with three moderate-level problems to complete in two hours. Another report mentions a 45-minute coding test before the interview. Not every candidate reports this stage.
The core technical round leans heavily on the projects and experience listed on your resume. SQL is the most frequently reported topic: joins, a second-highest-salary query, and a query for employees earning above the average salary. One candidate was questioned in depth on Power BI, covering Desktop versus Service, connection types, gateways, and sharing reports externally. Another report mentions Tableau. One candidate was asked how they would change an analytics app's schema or architecture to handle roughly ten times more dashboard requests.
Some candidates described extra technical questions beyond SQL and Power BI. These included basic OOPs and DBMS concepts, simple live Python problems using arrays and dictionaries, and high-level machine learning questions such as bagging versus boosting, regression algorithms, or LSTM. Which topics come up seems to depend on the resume and interviewer.
Behavioral or HR-style questions cover your self-introduction, strengths and weaknesses, why you want to join Infosys, career goals, and your exact contribution on a team. One candidate was also asked about handling a challenging customer or situation. Clear, concise communication is repeatedly described as mattering as much as technical depth.
