Tech Mahindra Data Engineer Interview Questions (2026)

1. 1Online Assessment (English + Aptitude + Coding)60 minMedium

   Timed test where English grammar/comprehension and a written email or essay task carry unusual weight, alongside quantitative aptitude and basic coding questions.

2. 2Technical Interview40 minMedium

   Panel covers programming and CS fundamentals, projects, and — for network-aligned roles — telecom basics like the OSI stack and call flows.

3. 3Telecom Domain Round45 minHard

   For experienced OSS/BSS, 5G, or network-engineering hires, a specialist round on charging/billing flows, provisioning, network protocols, or RAN/core architecture.

4. 4Managerial Round30 minMedium

   A delivery manager evaluates client-communication maturity, shift and travel flexibility, and handling of escalations on live telecom accounts.

5. 5HR Round25 minEasy

   Closing discussion on communication, relocation/shift willingness, compensation, and culture fit framed around the Mahindra Rise philosophy.

1. Q1

   Design the incremental pull for Tech Mahindra's utilities client's smart-meter reading feed. Which watermark column do you trust, and what happens when the source clock drifts?

   HardSQL roundincremental extractionTech Mahindra-specific

   Context: Tech Mahindra utilities client's smart-meter reading feed

   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 Tech Mahindra's travel client's booking and cancellation mart 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 loadTech Mahindra-specific

   Context: Tech Mahindra travel client's booking and cancellation mart

   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 Tech Mahindra's banking client's nightly core-banking ingestion. Write the SCD Type 2 merge and name what it costs.

   HardSQL roundslowly changing dimensionsTech Mahindra-specific

   Context: Tech Mahindra banking client's nightly core-banking ingestion

   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 Tech Mahindra's retail client's daily sales fact load tripled. Debug it and prove the fix.

   HardSQL roundjoin fan-out debuggingTech Mahindra-specific

   Context: Tech Mahindra retail client's daily sales fact load

   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 Tech Mahindra's telecom client's call-detail-record pipeline loads. What do you write instead of dropping the row?

   MediumSQL roundNULL and late-arriving keysTech Mahindra-specific

   Context: Tech Mahindra telecom client's call-detail-record 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 Tech Mahindra's insurance client's claims data lake scans the whole table every night. How do you make it cheap?

   MediumSQL roundquery performanceTech Mahindra-specific

   Context: Tech Mahindra insurance client's claims data lake

   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 Tech Mahindra's e-commerce client's clickstream ingestion runs for an hour while the rest finish in a minute. Diagnose and fix it.

   HardPython and Spark rounddata skewTech Mahindra-specific

   Context: Tech Mahindra e-commerce client's clickstream ingestion

   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 Tech Mahindra's healthcare client's patient-records batch feed does a groupBy on a billion rows. Where does the time go?

   HardPython and Spark roundshuffle and partitioningTech Mahindra-specific

   Context: Tech Mahindra healthcare client's patient-records batch feed

   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 Tech Mahindra's logistics client's shipment-tracking pipeline, and when does that blow up?

   MediumPython and Spark roundbroadcast joinsTech Mahindra-specific

   Context: Tech Mahindra logistics client's shipment-tracking pipeline

   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 Tech Mahindra's manufacturing client's sensor telemetry feed has hundreds of thousands of tiny files. What is the damage, and what is the fix?

    HardPython and Spark roundsmall files problemTech Mahindra-specific

    Context: Tech Mahindra manufacturing client's sensor telemetry feed

    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 Tech Mahindra's media client's subscriber-events pipeline with a Python UDF. When is that the wrong call?

    MediumPython and Spark roundUDFs vs built-insTech Mahindra-specific

    Context: Tech Mahindra media client's subscriber-events pipeline

    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 Tech Mahindra's utilities client's smart-meter reading feed is used four times. Do you cache it? Defend the answer.

    MediumPython and Spark roundcaching and reuseTech Mahindra-specific

    Context: Tech Mahindra utilities client's smart-meter reading feed

    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 Tech Mahindra's travel client's booking and cancellation mart in one sentence, and show what breaks when the grain is wrong.

    MediumData modelling roundgrain definitionTech Mahindra-specific

    Context: Tech Mahindra travel client's booking and cancellation mart

    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 Tech Mahindra's banking client's nightly core-banking ingestion as a star schema. Which tables are facts, which are dimensions, and why not one wide table?

    MediumData modelling roundstar schema designTech Mahindra-specific

    Context: Tech Mahindra banking client's nightly core-banking ingestion

    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 Tech Mahindra's retail client's daily sales fact load, and what is not allowed to happen in each.

    MediumData modelling roundlayered architectureTech Mahindra-specific

    Context: Tech Mahindra retail client's daily sales fact load

    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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