TCS Data Engineer Interview Questions (2026)

1. 1TCS NQT60 minMedium

   Online qualifier on TCS iON covering numerical/verbal/reasoning ability plus a coding section; the cutoff and score band route candidates to Ninja, Digital, or Prime interviews.

2. 2Technical Round (TR)40 minMedium

   Panel probes CS fundamentals (OOP, DBMS, OS basics), the candidate's final-year or work projects, and one or two simple programs written live; laterals get grilled on their claimed stack.

3. 3Digital/Prime Coding Interview45 minHard

   For Digital and Prime track candidates, a harder round with DSA problems, code optimization, and questions on cloud or newer technologies.

4. 4Managerial Round (MR)30 minMedium

   A senior manager tests composure with scenario and mild stress questions — missed deadlines, disagreement with a lead, willingness to work in any technology or location.

5. 5HR Round25 minEasy

   Discussion of the service agreement, relocation and shift flexibility, salary structure, and background verification documents.

1. Q1

   Design the incremental pull for TCS's banking client's nightly core-banking ingestion. Which watermark column do you trust, and what happens when the source clock drifts?

   HardSQL roundincremental extractionTCS-specific

   Context:

   TCS banking client's nightly core-banking 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 TCS's retail client's daily sales fact load 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 loadTCS-specific

   Context:

   TCS retail client's daily sales fact load

   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 TCS's telecom client's call-detail-record pipeline. Write the SCD Type 2 merge and name what it costs.

   HardSQL roundslowly changing dimensionsTCS-specific

   Context:

   TCS telecom client's call-detail-record 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 TCS's insurance client's claims data lake tripled. Debug it and prove the fix.

   HardSQL roundjoin fan-out debuggingTCS-specific

   Context:

   TCS insurance client's claims data lake

   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 TCS's e-commerce client's clickstream ingestion loads. What do you write instead of dropping the row?

   MediumSQL roundNULL and late-arriving keysTCS-specific

   Context:

   TCS e-commerce client's clickstream 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 TCS's healthcare client's patient-records batch feed scans the whole table every night. How do you make it cheap?

   MediumSQL roundquery performanceTCS-specific

   Context:

   TCS healthcare client's patient-records batch 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 TCS's logistics client's shipment-tracking pipeline runs for an hour while the rest finish in a minute. Diagnose and fix it.

   HardPython and Spark rounddata skewTCS-specific

   Context:

   TCS logistics client's shipment-tracking 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 TCS's manufacturing client's sensor telemetry feed does a groupBy on a billion rows. Where does the time go?

   HardPython and Spark roundshuffle and partitioningTCS-specific

   Context:

   TCS manufacturing client's sensor telemetry 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 TCS's media client's subscriber-events pipeline, and when does that blow up?

   MediumPython and Spark roundbroadcast joinsTCS-specific

   Context:

   TCS media client's subscriber-events 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 TCS's utilities client's smart-meter reading feed has hundreds of thousands of tiny files. What is the damage, and what is the fix?

    HardPython and Spark roundsmall files problemTCS-specific

    Context:

    TCS utilities client's smart-meter reading 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 TCS's travel client's booking and cancellation mart with a Python UDF. When is that the wrong call?

    MediumPython and Spark roundUDFs vs built-insTCS-specific

    Context:

    TCS travel client's booking and cancellation mart

    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 TCS's banking client's nightly core-banking ingestion is used four times. Do you cache it? Defend the answer.

    MediumPython and Spark roundcaching and reuseTCS-specific

    Context:

    TCS banking client's nightly core-banking 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 TCS's retail client's daily sales fact load in one sentence, and show what breaks when the grain is wrong.

    MediumData modelling roundgrain definitionTCS-specific

    Context:

    TCS retail client's daily sales fact load

    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 TCS's telecom client's call-detail-record pipeline as a star schema. Which tables are facts, which are dimensions, and why not one wide table?

    MediumData modelling roundstar schema designTCS-specific

    Context:

    TCS telecom client's call-detail-record 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 TCS's insurance client's claims data lake, and what is not allowed to happen in each.

    MediumData modelling roundlayered architectureTCS-specific

    Context:

    TCS insurance client's claims data lake

    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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