Deloitte Data Analyst Interview Questions (2026)

1. 1Online Assessment60 minMedium

   Timed aptitude, technical MCQs, and English proficiency sections that gate the interview shortlist.

2. 2JAM / Group Discussion25 minEasy

   Speak for a minute on a surprise topic or debate in a group; screens articulation and composure.

3. 3Technical Interview45 minMedium

   Projects, core fundamentals (SQL, OOP, or domain), and applied scenario questions for the service line.

4. 4Senior / Manager Round45 minMedium

   Manager runs a case-let or client scenario plus deep project probing to judge consulting readiness.

5. 5HR Discussion30 minEasy

   Values-fit conversation, compensation, location, and background verification expectations.

1. Q1

   Write a SQL query to find duplicate customer records in Deloitte's logistics client's delivery SLA tracking. How would you decide which record survives?

   MediumSQL roundduplicate detectionDeloitte-specific

   Context: Deloitte logistics client's delivery SLA tracking

   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 Deloitte's credit-card client's spend-category dashboard?

   MediumSQL roundwindow functionsDeloitte-specific

   Context: Deloitte credit-card client's spend-category 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 Deloitte's banking client's loan-portfolio reporting, when would an INNER JOIN silently drop rows a LEFT JOIN would keep? Show the row counts you would check.

   MediumSQL roundjoinsDeloitte-specific

   Context: Deloitte banking client's loan-portfolio 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 Deloitte's retail client's daily sales dashboard does not match the source system's total. How do you reconcile them?

   HardSQL roundaggregation and reconciliationDeloitte-specific

   Context: Deloitte retail client's daily sales dashboard

   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 Deloitte's telecom client's churn reporting contain NULLs — filters, joins, COUNT vs COUNT(col), averages?

   MediumSQL roundNULL handlingDeloitte-specific

   Context: Deloitte telecom client's churn 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 Deloitte's insurance client's claims data mart doubled. Debug the query and prove the fix.

   HardSQL roundjoin fan-out debuggingDeloitte-specific

   Context: Deloitte insurance client's claims data mart

   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 Deloitte's e-commerce client's returns analysis into CTEs, and what does it cost?

   MediumSQL roundCTEs and subqueriesDeloitte-specific

   Context: Deloitte e-commerce client's returns analysis

   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 Deloitte's healthcare client's appointment no-show reporting, handling ties explicitly.

   MediumSQL roundranking problemsDeloitte-specific

   Context: Deloitte healthcare client's appointment no-show 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 Deloitte's logistics client's delivery SLA tracking returns #N/A for valid IDs. What do you check?

   MediumExcel and BI roundlookupsDeloitte-specific

   Context: Deloitte logistics client's delivery SLA tracking

   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 Deloitte's credit-card client's spend-category dashboard: what goes in rows, values, and filters, and when does a pivot beat formulas?

    MediumExcel and BI roundpivot tablesDeloitte-specific

    Context: Deloitte credit-card client's spend-category 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 Deloitte's banking client's loan-portfolio reporting before any analysis.

    MediumExcel and BI rounddata cleaning in ExcelDeloitte-specific

    Context: Deloitte banking client's loan-portfolio 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 Deloitte's retail client's daily sales dashboard need a DAX measure instead of a calculated column?

    HardExcel and BI roundDAX measuresDeloitte-specific

    Context: Deloitte retail client's daily sales dashboard

    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 Deloitte's telecom client's churn reporting: which tables are facts, which are dimensions, and why not one flat table?

    MediumExcel and BI rounddata modelingDeloitte-specific

    Context: Deloitte telecom client's churn 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 Deloitte dashboard for insurance client's claims data mart takes a minute to load. How do you make it fast?

    MediumExcel and BI rounddashboard performanceDeloitte-specific

    Context: Deloitte insurance client's claims data mart

    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 Deloitte's e-commerce client's returns analysis, and what do you deliberately leave out?

    MediumExcel and BI rounddashboard designDeloitte-specific

    Context: Deloitte e-commerce client's returns analysis

    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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