Wipro Data Engineer Interview Questions (2026)

1. 1Elite NTH Online Test60 minMedium

   Online assessment with aptitude/logical/verbal sections, a written essay to test business English, and two coding problems in a choice of languages.

2. 2Turbo Coding Challenge60 minHard

   Harder timed coding round for the premium Turbo package, with DSA problems demanding fully working, efficient solutions.

3. 3Technical Interview40 minMedium

   Panel covers programming fundamentals, OOP, SQL, projects, and simple code on demand; laterals are quizzed on the client account's specific technologies.

4. 4Managerial Round35 minMedium

   For experienced hires, a delivery/account manager explores ownership of deliverables, escalation handling, and team situations on past projects.

5. 5HR Round25 minEasy

   Discussion of Spirit of Wipro fit, integrity questions, relocation and shift willingness, service agreement, and compensation.

1. Q1

   Design the incremental pull for Wipro's e-commerce client's clickstream ingestion. Which watermark column do you trust, and what happens when the source clock drifts?

   HardSQL roundincremental extractionWipro-specific

   Context: Wipro e-commerce client's clickstream ingestion

   How to answer: Pick an audit column the source actually maintains, overlap the window, and dedupe on a business key rather than assuming exactly-once. State the requirement, the data you would move, and the checks that make the output trustworthy. Walk the design concretely: sources, storage layout, transformations, schedule, and the failure cases. Close with how you would validate the result and hand it over to whoever runs it next.

2. Q2

   Rows in Wipro's healthcare client's patient-records batch feed arrive more than once after a retry. Write the SQL that lands exactly one row per business key and say which one survives.

   MediumSQL rounddeduplication on loadWipro-specific

   Context: Wipro healthcare client's patient-records batch feed

   How to answer: ROW_NUMBER over the business key ordered by load or update time, with an explicit survivorship rule you can defend to the client. State the requirement, the data you would move, and the checks that make the output trustworthy. Walk the design concretely: sources, storage layout, transformations, schedule, and the failure cases. Close with how you would validate the result and hand it over to whoever runs it next.

3. Q3

   The client wants history preserved on the customer dimension behind Wipro's logistics client's shipment-tracking pipeline. Write the SCD Type 2 merge and name what it costs.

   HardSQL roundslowly changing dimensionsWipro-specific

   Context: Wipro logistics client's shipment-tracking pipeline

   How to answer: Effective-from/to plus a current flag, hash-diff on tracked columns, and the row growth and query complexity you accept in return. State the requirement, the data you would move, and the checks that make the output trustworthy. Walk the design concretely: sources, storage layout, transformations, schedule, and the failure cases. Close with how you would validate the result and hand it over to whoever runs it next.

4. Q4

   After a new join, the fact row count in Wipro's manufacturing client's sensor telemetry feed tripled. Debug it and prove the fix.

   HardSQL roundjoin fan-out debuggingWipro-specific

   Context: Wipro manufacturing client's sensor telemetry feed

   How to answer: Find the one-to-many side, check key uniqueness first, pre-aggregate or dedupe, then verify with counts before and after. State the requirement, the data you would move, and the checks that make the output trustworthy. Walk the design concretely: sources, storage layout, transformations, schedule, and the failure cases. Close with how you would validate the result and hand it over to whoever runs it next.

5. Q5

   A dimension key is missing when the fact for Wipro's media client's subscriber-events pipeline loads. What do you write instead of dropping the row?

   MediumSQL roundNULL and late-arriving keysWipro-specific

   Context: Wipro media client's subscriber-events pipeline

   How to answer: Route to an unknown-member key, keep the fact, and reprocess when the dimension lands — never silently lose revenue rows. State the requirement, the data you would move, and the checks that make the output trustworthy. Walk the design concretely: sources, storage layout, transformations, schedule, and the failure cases. Close with how you would validate the result and hand it over to whoever runs it next.

6. Q6

   The reconciliation query over Wipro's utilities client's smart-meter reading feed scans the whole table every night. How do you make it cheap?

   MediumSQL roundquery performanceWipro-specific

   Context: Wipro utilities client's smart-meter reading feed

   How to answer: Partition pruning, predicate pushdown, and clustering on the filter column; read the plan before changing anything. State the requirement, the data you would move, and the checks that make the output trustworthy. Walk the design concretely: sources, storage layout, transformations, schedule, and the failure cases. Close with how you would validate the result and hand it over to whoever runs it next.

7. Q7

   One Spark task in Wipro's travel client's booking and cancellation mart runs for an hour while the rest finish in a minute. Diagnose and fix it.

   HardPython and Spark rounddata skewWipro-specific

   Context: Wipro travel client's booking and cancellation mart

   How to answer: Inspect partition sizes for a hot key, then salt, broadcast the small side, or repartition — and say which one you would try first and why. State the requirement, the data you would move, and the checks that make the output trustworthy. Walk the design concretely: sources, storage layout, transformations, schedule, and the failure cases. Close with how you would validate the result and hand it over to whoever runs it next.

8. Q8

   Walk through what actually happens when Wipro's banking client's nightly core-banking ingestion does a groupBy on a billion rows. Where does the time go?

   HardPython and Spark roundshuffle and partitioningWipro-specific

   Context: Wipro banking client's nightly core-banking ingestion

   How to answer: Wide vs narrow transformations, shuffle write and read, partition count, and spill to disk. State the requirement, the data you would move, and the checks that make the output trustworthy. Walk the design concretely: sources, storage layout, transformations, schedule, and the failure cases. Close with how you would validate the result and hand it over to whoever runs it next.

9. Q9

   When would you broadcast the dimension in Wipro's retail client's daily sales fact load, and when does that blow up?

   MediumPython and Spark roundbroadcast joinsWipro-specific

   Context: Wipro retail client's daily sales fact load

   How to answer: Small-side size against executor memory, the auto-broadcast threshold, and the driver OOM you get when you force it on a large table. State the requirement, the data you would move, and the checks that make the output trustworthy. Walk the design concretely: sources, storage layout, transformations, schedule, and the failure cases. Close with how you would validate the result and hand it over to whoever runs it next.

10. Q10

    The landing zone for Wipro's telecom client's call-detail-record pipeline has hundreds of thousands of tiny files. What is the damage, and what is the fix?

    HardPython and Spark roundsmall files problemWipro-specific

    Context: Wipro telecom client's call-detail-record pipeline

    How to answer: Listing and task overhead, then compaction, target file sizes, and fixing the writer rather than compacting forever. State the requirement, the data you would move, and the checks that make the output trustworthy. Walk the design concretely: sources, storage layout, transformations, schedule, and the failure cases. Close with how you would validate the result and hand it over to whoever runs it next.

11. Q11

    A teammate solved a transformation in Wipro's insurance client's claims data lake with a Python UDF. When is that the wrong call?

    MediumPython and Spark roundUDFs vs built-insWipro-specific

    Context: Wipro insurance client's claims data lake

    How to answer: Serialization cost and the lost optimizer pushdown; reach for built-in functions first, then pandas UDFs if you must. State the requirement, the data you would move, and the checks that make the output trustworthy. Walk the design concretely: sources, storage layout, transformations, schedule, and the failure cases. Close with how you would validate the result and hand it over to whoever runs it next.

12. Q12

    The same intermediate DataFrame in Wipro's e-commerce client's clickstream ingestion is used four times. Do you cache it? Defend the answer.

    MediumPython and Spark roundcaching and reuseWipro-specific

    Context: Wipro e-commerce client's clickstream ingestion

    How to answer: Recompute cost against memory pressure and eviction; measure with the DAG rather than caching by reflex. State the requirement, the data you would move, and the checks that make the output trustworthy. Walk the design concretely: sources, storage layout, transformations, schedule, and the failure cases. Close with how you would validate the result and hand it over to whoever runs it next.

13. Q13

    State the grain of the fact table behind Wipro's healthcare client's patient-records batch feed in one sentence, and show what breaks when the grain is wrong.

    MediumData modelling roundgrain definitionWipro-specific

    Context: Wipro healthcare client's patient-records batch feed

    How to answer: One row equals one what; a mixed grain gives double counting no downstream fix can rescue. State the requirement, the data you would move, and the checks that make the output trustworthy. Walk the design concretely: sources, storage layout, transformations, schedule, and the failure cases. Close with how you would validate the result and hand it over to whoever runs it next.

14. Q14

    Model Wipro's logistics client's shipment-tracking pipeline as a star schema. Which tables are facts, which are dimensions, and why not one wide table?

    MediumData modelling roundstar schema designWipro-specific

    Context: Wipro logistics client's shipment-tracking pipeline

    How to answer: Conformed dimensions, additive measures, and the update and consistency cost of a single flattened table. State the requirement, the data you would move, and the checks that make the output trustworthy. Walk the design concretely: sources, storage layout, transformations, schedule, and the failure cases. Close with how you would validate the result and hand it over to whoever runs it next.

15. Q15

    Explain the bronze, silver and gold layers for Wipro's manufacturing client's sensor telemetry feed, and what is not allowed to happen in each.

    MediumData modelling roundlayered architectureWipro-specific

    Context: Wipro manufacturing client's sensor telemetry feed

    How to answer: Raw fidelity in bronze, conformed and deduplicated in silver, business-shaped aggregates in gold; no business logic hidden in ingestion. State the requirement, the data you would move, and the checks that make the output trustworthy. Walk the design concretely: sources, storage layout, transformations, schedule, and the failure cases. Close with how you would validate the result and hand it over to whoever runs it next.

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