LTIMindtree Data Analyst Interview Questions (2026)

Online assessment -> technical interview -> managerial & HR round

1. Q1

   Write a SQL query to find duplicate customer records in LTIMindtree's banking client's loan-portfolio reporting. How would you decide which record survives?

   MediumSQL roundduplicate detectionLTIMindtree-specific

   Context: LTIMindtree banking client's loan-portfolio reporting

   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 LTIMindtree's retail client's daily sales dashboard?

   MediumSQL roundwindow functionsLTIMindtree-specific

   Context: LTIMindtree retail client's daily sales dashboard

   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 LTIMindtree's telecom client's churn reporting, when would an INNER JOIN silently drop rows a LEFT JOIN would keep? Show the row counts you would check.

   MediumSQL roundjoinsLTIMindtree-specific

   Context: LTIMindtree telecom client's churn reporting

   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 LTIMindtree's insurance client's claims data mart does not match the source system's total. How do you reconcile them?

   HardSQL roundaggregation and reconciliationLTIMindtree-specific

   Context: LTIMindtree insurance client's claims data mart

   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 LTIMindtree's e-commerce client's returns analysis contain NULLs — filters, joins, COUNT vs COUNT(col), averages?

   MediumSQL roundNULL handlingLTIMindtree-specific

   Context: LTIMindtree e-commerce client's returns analysis

   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 LTIMindtree's healthcare client's appointment no-show reporting doubled. Debug the query and prove the fix.

   HardSQL roundjoin fan-out debuggingLTIMindtree-specific

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

   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 LTIMindtree's logistics client's delivery SLA tracking into CTEs, and what does it cost?

   MediumSQL roundCTEs and subqueriesLTIMindtree-specific

   Context: LTIMindtree logistics client's delivery SLA tracking

   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 LTIMindtree's credit-card client's spend-category dashboard, handling ties explicitly.

   MediumSQL roundranking problemsLTIMindtree-specific

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

   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 LTIMindtree's banking client's loan-portfolio reporting returns #N/A for valid IDs. What do you check?

   MediumExcel and BI roundlookupsLTIMindtree-specific

   Context: LTIMindtree banking client's loan-portfolio reporting

   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 LTIMindtree's retail client's daily sales dashboard: what goes in rows, values, and filters, and when does a pivot beat formulas?

    MediumExcel and BI roundpivot tablesLTIMindtree-specific

    Context: LTIMindtree retail client's daily sales dashboard

    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 LTIMindtree's telecom client's churn reporting before any analysis.

    MediumExcel and BI rounddata cleaning in ExcelLTIMindtree-specific

    Context: LTIMindtree telecom client's churn reporting

    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 LTIMindtree's insurance client's claims data mart need a DAX measure instead of a calculated column?

    HardExcel and BI roundDAX measuresLTIMindtree-specific

    Context: LTIMindtree insurance client's claims data mart

    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 LTIMindtree's e-commerce client's returns analysis: which tables are facts, which are dimensions, and why not one flat table?

    MediumExcel and BI rounddata modelingLTIMindtree-specific

    Context: LTIMindtree e-commerce client's returns analysis

    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 LTIMindtree dashboard for healthcare client's appointment no-show reporting takes a minute to load. How do you make it fast?

    MediumExcel and BI rounddashboard performanceLTIMindtree-specific

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

    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 LTIMindtree's logistics client's delivery SLA tracking, and what do you deliberately leave out?

    MediumExcel and BI rounddashboard designLTIMindtree-specific

    Context: LTIMindtree logistics client's delivery SLA tracking

    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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Revise programming, basic DSA and SQL; prepare project deep-dives and HR answers

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

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