Infosys Data Analyst Interview Questions (2026)

1. 1Infosys Online Assessment60 minMedium

   Timed test with logical reasoning, verbal ability, quantitative aptitude, and the well-known pseudocode section that decides interview shortlisting and track.

2. 2Power Programmer (SP) Coding Round50 minHard

   For HackWithInfy/InfyTQ-routed candidates, a competitive-programming style interview with 2-3 DSA problems requiring working code and complexity analysis.

3. 3Technical Interview40 minMedium

   Panel covers OOP, DBMS and SQL queries, one or two programs or pseudocode on paper/screen, and a walkthrough of academic or work projects.

4. 4Techno-Managerial Round40 minMedium

   For laterals, a delivery manager probes system-level decisions on past projects, estimation, client communication, and how the candidate handles production issues.

5. 5HR Round25 minEasy

   Values-and-stability conversation covering why Infosys, C-LIFE values awareness, relocation, training agreement, and salary/joining logistics.

1. Q1

   For Infosys's insurance client's claims data mart, when would an INNER JOIN silently drop rows a LEFT JOIN would keep? Show the row counts you would check.

   MediumSQL roundjoinsInfosys-specific

   Context:

   Infosys insurance client's claims data mart

   How to answer:

   Explain join semantics with unmatched keys, and the count-before/count-after habit. State the business question, the data you would pull, and the checks that make the number trustworthy. Walk through the query or steps concretely, naming tables, joins, filters, and edge cases. Close with how you would validate the result and explain it to the stakeholder.

2. Q2

   Your monthly GROUP BY summary for Infosys's e-commerce client's returns analysis does not match the source system's total. How do you reconcile them?

   HardSQL roundaggregation and reconciliationInfosys-specific

   Context:

   Infosys e-commerce client's returns analysis

   How to answer:

   Compare grain, filters, timezone/date boundaries, late-arriving rows, and duplicates. State the business question, the data you would pull, and the checks that make the number trustworthy. Walk through the query or steps concretely, naming tables, joins, filters, and edge cases. Close with how you would validate the result and explain it to the stakeholder.

3. Q3

   Which results change when key columns in Infosys's healthcare client's appointment no-show reporting contain NULLs — filters, joins, COUNT vs COUNT(col), averages?

   MediumSQL roundNULL handlingInfosys-specific

   Context:

   Infosys healthcare client's appointment no-show reporting

   How to answer:

   Walk NULL semantics through WHERE, JOIN keys, aggregates, and COALESCE defaults. State the business question, the data you would pull, and the checks that make the number trustworthy. Walk through the query or steps concretely, naming tables, joins, filters, and edge cases. Close with how you would validate the result and explain it to the stakeholder.

4. Q4

   After adding a join, revenue in Infosys's logistics client's delivery SLA tracking doubled. Debug the query and prove the fix.

   HardSQL roundjoin fan-out debuggingInfosys-specific

   Context:

   Infosys logistics client's delivery SLA tracking

   How to answer:

   Detect one-to-many fan-out, pre-aggregate or dedupe the many side, verify with row counts. State the business question, the data you would pull, and the checks that make the number trustworthy. Walk through the query or steps concretely, naming tables, joins, filters, and edge cases. Close with how you would validate the result and explain it to the stakeholder.

5. Q5

   When would you restructure a nested-subquery report for Infosys's credit-card client's spend-category dashboard into CTEs, and what does it cost?

   MediumSQL roundCTEs and subqueriesInfosys-specific

   Context:

   Infosys credit-card client's spend-category dashboard

   How to answer:

   Readability, reuse, debugging layer by layer; note optimizer behavior differences. State the business question, the data you would pull, and the checks that make the number trustworthy. Walk through the query or steps concretely, naming tables, joins, filters, and edge cases. Close with how you would validate the result and explain it to the stakeholder.

6. Q6

   Find the top 3 products per region by revenue in Infosys's banking client's loan-portfolio reporting, handling ties explicitly.

   MediumSQL roundranking problemsInfosys-specific

   Context:

   Infosys banking client's loan-portfolio reporting

   How to answer:

   ROW_NUMBER vs RANK vs DENSE_RANK, and which tie behavior the business actually wants. State the business question, the data you would pull, and the checks that make the number trustworthy. Walk through the query or steps concretely, naming tables, joins, filters, and edge cases. Close with how you would validate the result and explain it to the stakeholder.

7. Q7

   List the cleaning steps you would run on a raw extract for Infosys's insurance client's claims data mart before any analysis.

   MediumExcel and BI rounddata cleaning in ExcelInfosys-specific

   Context:

   Infosys insurance client's claims data mart

   How to answer:

   Duplicates, blanks, types, date formats, merged cells, and a repeatable order of operations. State the business question, the data you would pull, and the checks that make the number trustworthy. Walk through the query or steps concretely, naming tables, joins, filters, and edge cases. Close with how you would validate the result and explain it to the stakeholder.

8. Q8

   A key metric in Infosys's banking client's loan-portfolio reporting fell 15% week over week. Walk through your investigation before you alert the client.

   HardAnalytics case roundmetric drop investigationInfosys-specific

   Context:

   Infosys banking client's loan-portfolio reporting

   How to answer:

   Data issue first, then mix shift, seasonality, one-segment vs broad, and a finding the client can act on. State the business question, the data you would pull, and the checks that make the number trustworthy. Walk through the query or steps concretely, naming tables, joins, filters, and edge cases. Close with how you would validate the result and explain it to the stakeholder.

9. Q9

   The client sees that customers using feature X in Infosys's retail client's daily sales dashboard churn less and wants to force-enroll everyone. What do you say?

   MediumAnalytics case roundcorrelation vs causationInfosys-specific

   Context:

   Infosys retail client's daily sales dashboard

   How to answer:

   Selection bias, confounders, and what evidence would actually support the rollout. State the business question, the data you would pull, and the checks that make the number trustworthy. Walk through the query or steps concretely, naming tables, joins, filters, and edge cases. Close with how you would validate the result and explain it to the stakeholder.

10. Q10

    The client ran a promotion in two regions for Infosys's telecom client's churn reporting. How do you judge whether it worked?

    MediumAnalytics case roundcampaign evaluationInfosys-specific

    Context:

    Infosys telecom client's churn reporting

    How to answer:

    Comparable baseline, control regions, pre/post windows, and honest uncertainty. State the business question, the data you would pull, and the checks that make the number trustworthy. Walk through the query or steps concretely, naming tables, joins, filters, and edge cases. Close with how you would validate the result and explain it to the stakeholder.

11. Q11

    Present your findings on Infosys's e-commerce client's returns analysis to a client stakeholder who does not read SQL. Structure the 10 minutes.

    MediumAnalytics case rounddata storytellingInfosys-specific

    Context:

    Infosys e-commerce client's returns analysis

    How to answer:

    Answer first, three supporting views, caveats in plain words, and a clear ask. State the business question, the data you would pull, and the checks that make the number trustworthy. Walk through the query or steps concretely, naming tables, joins, filters, and edge cases. Close with how you would validate the result and explain it to the stakeholder.

12. Q12

    How do you detect and treat outliers in Infosys's logistics client's delivery SLA tracking without hiding real events?

    MediumStatistics and metrics roundoutlier treatmentInfosys-specific

    Context:

    Infosys logistics client's delivery SLA tracking

    How to answer:

    IQR/z-score detection, investigate before excluding, and document every exclusion. State the business question, the data you would pull, and the checks that make the number trustworthy. Walk through the query or steps concretely, naming tables, joins, filters, and edge cases. Close with how you would validate the result and explain it to the stakeholder.

13. Q13

    Define the primary KPI and two guardrail metrics for Infosys's banking client's loan-portfolio reporting, with exact formulas.

    MediumStatistics and metrics rounddefining KPIsInfosys-specific

    Context:

    Infosys banking client's loan-portfolio reporting

    How to answer:

    Numerator, denominator, grain, and the gaming behavior each guardrail prevents. State the business question, the data you would pull, and the checks that make the number trustworthy. Walk through the query or steps concretely, naming tables, joins, filters, and edge cases. Close with how you would validate the result and explain it to the stakeholder.

14. Q14

    The client escalates: their internal report disagrees with your dashboard for Infosys's insurance client's claims data mart. How do you close the gap?

    HardData quality rounddashboard mismatch escalationInfosys-specific

    Context:

    Infosys insurance client's claims data mart

    How to answer:

    Reproduce both numbers, diff definitions and filters, agree one source of truth in writing. State the business question, the data you would pull, and the checks that make the number trustworthy. Walk through the query or steps concretely, naming tables, joins, filters, and edge cases. Close with how you would validate the result and explain it to the stakeholder.

15. Q15

    Tell me about an analysis project similar to Infosys's e-commerce client's returns analysis. What did you build, what went wrong, and what changed because of it?

    EasyBehavioral/project discussionproject deep diveInfosys-specific

    Context:

    Infosys e-commerce client's returns analysis

    How to answer:

    Concrete project, your ownership, one failure honestly told, and the decision your work drove. State the business question, the data you would pull, and the checks that make the number trustworthy. Walk through the query or steps concretely, naming tables, joins, filters, and edge cases. Close with how you would validate the result and explain it to the stakeholder.

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