HCLTech Data Engineer Interview Questions (2026)

1. 1Online Assessment60 minMedium

   Aptitude, reasoning, and verbal sections plus technical MCQs (and coding for developer roles) used to shortlist for interviews.

2. 2Technical Interview40 minMedium

   Panel mixes programming fundamentals with OS, networking, and troubleshooting questions reflecting HCL's infra strength, plus project walkthroughs.

3. 3ER&D Domain Round45 minHard

   For engineering-services roles, a specialist round on embedded C/C++, protocols, or the specific product-engineering domain (automotive, medical, telecom).

4. 4Techno-Managerial Round35 minMedium

   For laterals, a manager checks incident/escalation handling, on-call and shift experience, and ownership on client deliverables.

5. 5HR Round25 minEasy

   Standard discussion covering shift and relocation flexibility, notice period or joining date, salary, and document verification.

1. Q1

   Design the incremental pull for HCLTech's manufacturing client's sensor telemetry feed. Which watermark column do you trust, and what happens when the source clock drifts?

   HardSQL roundincremental extractionHCLTech-specific

   Context:

   HCLTech manufacturing client's sensor telemetry 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 HCLTech's media client's subscriber-events pipeline 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 loadHCLTech-specific

   Context:

   HCLTech media client's subscriber-events pipeline

   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 HCLTech's utilities client's smart-meter reading feed. Write the SCD Type 2 merge and name what it costs.

   HardSQL roundslowly changing dimensionsHCLTech-specific

   Context:

   HCLTech utilities client's smart-meter reading feed

   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 HCLTech's travel client's booking and cancellation mart tripled. Debug it and prove the fix.

   HardSQL roundjoin fan-out debuggingHCLTech-specific

   Context:

   HCLTech travel client's booking and cancellation mart

   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 HCLTech's banking client's nightly core-banking ingestion loads. What do you write instead of dropping the row?

   MediumSQL roundNULL and late-arriving keysHCLTech-specific

   Context:

   HCLTech banking client's nightly core-banking ingestion

   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 HCLTech's retail client's daily sales fact load scans the whole table every night. How do you make it cheap?

   MediumSQL roundquery performanceHCLTech-specific

   Context:

   HCLTech retail client's daily sales fact load

   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 HCLTech's telecom client's call-detail-record pipeline runs for an hour while the rest finish in a minute. Diagnose and fix it.

   HardPython and Spark rounddata skewHCLTech-specific

   Context:

   HCLTech telecom client's call-detail-record pipeline

   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 HCLTech's insurance client's claims data lake does a groupBy on a billion rows. Where does the time go?

   HardPython and Spark roundshuffle and partitioningHCLTech-specific

   Context:

   HCLTech insurance client's claims data lake

   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 HCLTech's e-commerce client's clickstream ingestion, and when does that blow up?

   MediumPython and Spark roundbroadcast joinsHCLTech-specific

   Context:

   HCLTech e-commerce client's clickstream ingestion

   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 HCLTech's healthcare client's patient-records batch feed has hundreds of thousands of tiny files. What is the damage, and what is the fix?

    HardPython and Spark roundsmall files problemHCLTech-specific

    Context:

    HCLTech healthcare client's patient-records batch 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 HCLTech's logistics client's shipment-tracking pipeline with a Python UDF. When is that the wrong call?

    MediumPython and Spark roundUDFs vs built-insHCLTech-specific

    Context:

    HCLTech logistics client's shipment-tracking 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 HCLTech's manufacturing client's sensor telemetry feed is used four times. Do you cache it? Defend the answer.

    MediumPython and Spark roundcaching and reuseHCLTech-specific

    Context:

    HCLTech manufacturing client's sensor telemetry 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 HCLTech's media client's subscriber-events pipeline in one sentence, and show what breaks when the grain is wrong.

    MediumData modelling roundgrain definitionHCLTech-specific

    Context:

    HCLTech media client's subscriber-events pipeline

    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 HCLTech's utilities client's smart-meter reading feed as a star schema. Which tables are facts, which are dimensions, and why not one wide table?

    MediumData modelling roundstar schema designHCLTech-specific

    Context:

    HCLTech utilities client's smart-meter reading feed

    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 HCLTech's travel client's booking and cancellation mart, and what is not allowed to happen in each.

    MediumData modelling roundlayered architectureHCLTech-specific

    Context:

    HCLTech travel client's booking and cancellation mart

    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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