Infosys Data and Business Analytics interview typically runs 2-3 rounds: recruiter screening, aptitude/online assessment, technical interview, and HR round. It usually takes a few days to weeks and is notably basics-focused and resume-driven.
$105K
Avg. Base Comp
$107K
Avg. Total Comp
3-4
Typical Rounds
2-4 weeks
Process Length
This guide is framed as a Data and Business Analytics interview because the available evidence sits in the broader analytics family rather than a cleanly separate Data Analyst lane.
Our candidates report that Infosys is far less interested in flashy problem-solving than in whether you can explain the fundamentals cleanly and tie them back to real work. Across experiences, the strongest signal was resume ownership: interviewers repeatedly dug into listed projects, daily responsibilities, and exact contributions, and candidates who could walk through those details confidently tended to do well. Even when the tone was calm and conversational, the bar was still there — not for advanced algorithms, but for crisp explanations of SQL, DBMS, OOPs, and core analytics concepts.
A recurring theme is that the company seems to value practical fluency over depth for depth’s sake. We saw basic SQL prompts like joins and second-highest salary, plus familiar ML questions such as bagging vs. boosting or LSTM, but the expectation was usually a high-level explanation rather than a research-level discussion. For analyst candidates, Power BI came up as a meaningful differentiator, especially around Desktop vs. Service, connection modes, gateways, and sharing reports externally. That tells us Infosys is checking whether you can operate in a client-facing, delivery-oriented environment where tools and communication matter as much as theory.
One non-obvious pattern: several candidates described the interviews as straightforward but somewhat uneven in depth, with some interviewers staying close to basics and others pushing a bit beyond the role title. That means the safest preparation is not memorizing obscure tricks, but being ready to defend every line on your resume and answer simple questions without hesitation. Candidates who sounded structured, specific, and comfortable under pressure generally came away better than those who overcomplicated answers or leaned on buzzwords.
Synthetized from 4 candidates reports by our editorial team.
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Real interview reports from people who went through the Infosys process.
The hardest part of my Infosys Data Analyst interview was realizing it was less about deep coding and more about how comfortably I could handle the basics under pressure. I spoke with two senior members of the analytics team, and they were polite and sharp throughout. The conversation felt structured but not overly formal, and the questions stayed in the easy-to-medium range overall. They started with an introduction and a few questions about my work experience, then moved into basic technical topics. I was asked to explain bagging and boosting, what LSTM is, and to talk through regression algorithms. Those were the most technical questions I got, and while I could answer some of them well, a few were definitely more challenging than I expected for a Data Analyst role.
There was also a shorter round that felt more like a face-to-face discussion than a heavy technical interview. In that part, they asked basic SQL and a few behavioral or HR-style questions, and one interview was only about 10 to 15 minutes long with some Python and SQL basics plus time for me to ask questions at the end. The process seemed to have around two to three rounds, and the main frustration was that it could take a while after each round to hear whether I was moving forward. My overall impression was that the interviews themselves were manageable and answerable, but the process felt slow and, in my case, I didn’t get an offer. If you’re preparing, I’d focus on being crisp with basic SQL, common HR questions, and being able to explain core ML concepts at a high level without overcomplicating them.
Prep tip from this candidate
Be ready for basic SQL plus short behavioral questions, and don’t ignore simple ML theory like bagging vs. boosting, LSTM, and regression. The face-to-face round sounded especially lightweight, so practice giving clear, concise answers rather than long technical deep-dives.
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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 | |
| P-value to a Layman | |
| Covariance vs Correlation | |
| Hurdles In Data Projects | |
| Find Duplicate Numbers in a List | |
| Digitizing Student Test Scores | |
| Classification and Regression | |
| Ticket Agent Analysis | |
| Swap Variables | |
| Check Matching Parentheses | |
| String Palindromes | |
| Testing Constraints | |
| Why Do You Want to Work With Us | |
| Relational Migration | |
| Your Strengths and Weaknesses | |
| Empty Neighborhoods | |
| Employee Salaries | |
| Rolling Bank Transactions | |
| Customer Orders | |
| Comments Histogram | |
| Closest SAT Scores | |
| Prime to N | |
| Experiment Validity | |
| Monthly Customer Report | |
| First Touch Attribution | |
| First to Six |
Synthesized from candidate reports. Individual experiences may vary.
A phone screening with a recruiter to confirm availability, language skills, and general fit for the Data Analyst role. In some cases, this stage also includes a brief discussion of background and basic role expectations.
Candidates complete an assessment that can include English, basic logic, and sometimes coding and SQL questions. This stage appears to be a screening step before the main interviews.
A panel or interviewer spends most of the time going through your resume and projects in detail, asking you to explain your contributions, decisions, and learnings. Technical questions are usually fundamentals-focused, such as SQL, OOPs, DBMS, Power BI, and simple coding questions like second highest salary.
A shorter round focused on behavioral and situational questions, such as strengths, why you should be hired, handling challenges, and basic company or role fit. Some candidates described this as a face-to-face discussion with basic SQL or Python mixed in.