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

   Write a SQL query to find duplicate customer records in Infosys's retail client's daily sales dashboard. How would you decide which record survives?

   MediumSQL roundduplicate detectionInfosys-specific

   Context: Infosys retail client's daily sales dashboard

   How to answer: Use GROUP BY/HAVING or ROW_NUMBER over a business key, define survivorship rules, and push the fix upstream. 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

   Using window functions, how would you add a 7-day rolling total and a rank within category to Infosys's telecom client's churn reporting?

   MediumSQL roundwindow functionsInfosys-specific

   Context: Infosys telecom client's churn reporting

   How to answer: Contrast aggregate vs window behavior, frame clauses, and partition choices. 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

   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.

4. Q4

   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.

5. Q5

   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.

6. Q6

   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.

7. Q7

   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.

8. Q8

   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.

9. Q9

   Your XLOOKUP against the master sheet in Infosys's retail client's daily sales dashboard returns #N/A for valid IDs. What do you check?

   MediumExcel and BI roundlookupsInfosys-specific

   Context: Infosys retail client's daily sales dashboard

   How to answer: Trailing spaces, number-vs-text types, exact match mode, and TRIM/VALUE cleanup. 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

    Build a pivot summary for Infosys's telecom client's churn reporting: what goes in rows, values, and filters, and when does a pivot beat formulas?

    MediumExcel and BI roundpivot tablesInfosys-specific

    Context: Infosys telecom client's churn reporting

    How to answer: Structure the source as a flat table, choose aggregations, and know pivot refresh limits. 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

    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.

12. Q12

    In Power BI, when does Infosys's e-commerce client's returns analysis need a DAX measure instead of a calculated column?

    HardExcel and BI roundDAX measuresInfosys-specific

    Context: Infosys e-commerce client's returns analysis

    How to answer: Row context vs filter context, storage cost, and totals that only measures get right. 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

    Design the Power BI model for Infosys's healthcare client's appointment no-show reporting: which tables are facts, which are dimensions, and why not one flat table?

    MediumExcel and BI rounddata modelingInfosys-specific

    Context: Infosys healthcare client's appointment no-show reporting

    How to answer: Star schema, relationship directions, and what breaks when everything is one wide table. 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 says the Infosys dashboard for logistics client's delivery SLA tracking takes a minute to load. How do you make it fast?

    MediumExcel and BI rounddashboard performanceInfosys-specific

    Context: Infosys logistics client's delivery SLA tracking

    How to answer: Reduce cardinality, trim visuals, pre-aggregate, and measure before optimizing. 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

    Which KPIs go on the first screen of Infosys's credit-card client's spend-category dashboard, and what do you deliberately leave out?

    MediumExcel and BI rounddashboard designInfosys-specific

    Context: Infosys credit-card client's spend-category dashboard

    How to answer: Lead with decisions the client makes weekly; park detail in drill-throughs. 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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Clear the online test; revise coding fundamentals and projects; prepare HR/behavioral

Indicative Data Analyst pay in India: ~₹6–22 LPA (role-level range, not a Infosys-specific figure).

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