Accenture Data Engineer Interview Questions (2026)

1. 1Cognitive & Technical Assessment60 minMedium

   Timed online MCQs on aptitude, English, pseudocode, networking, and MS Office/cloud fundamentals.

2. 2Coding Assessment45 minMedium

   1-2 straightforward coding problems in a language of choice; correctness over optimization.

3. 3Communication Assessment30 minEasy

   Automated spoken-English evaluation (listening, pronunciation, fluency) that gates client-facing readiness.

4. 4Technical Interview45 minMedium

   Project walk-through, language/skill-track fundamentals, and scenario questions for the assigned practice.

5. 5Managerial / Client-Fit Round45 minMedium

   Delivery manager probes client-handling scenarios, estimation, escalations, and team leadership for experienced hires.

6. 6HR Discussion30 minEasy

   Compensation, location/shift flexibility, relocation, and joining timeline; light behavioral questions.

1. Q1

   Design the incremental pull for Accenture's telecom client's call-detail-record pipeline. Which watermark column do you trust, and what happens when the source clock drifts?

   HardSQL roundincremental extractionAccenture-specific

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

   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 Accenture's insurance client's claims data lake 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 loadAccenture-specific

   Context: Accenture insurance client's claims data lake

   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 Accenture's e-commerce client's clickstream ingestion. Write the SCD Type 2 merge and name what it costs.

   HardSQL roundslowly changing dimensionsAccenture-specific

   Context: Accenture e-commerce client's clickstream 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 Accenture's healthcare client's patient-records batch feed tripled. Debug it and prove the fix.

   HardSQL roundjoin fan-out debuggingAccenture-specific

   Context: Accenture healthcare client's patient-records batch 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 Accenture's logistics client's shipment-tracking pipeline loads. What do you write instead of dropping the row?

   MediumSQL roundNULL and late-arriving keysAccenture-specific

   Context: Accenture logistics client's shipment-tracking 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 Accenture's manufacturing client's sensor telemetry feed scans the whole table every night. How do you make it cheap?

   MediumSQL roundquery performanceAccenture-specific

   Context: Accenture manufacturing client's sensor telemetry 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 Accenture's media client's subscriber-events pipeline runs for an hour while the rest finish in a minute. Diagnose and fix it.

   HardPython and Spark rounddata skewAccenture-specific

   Context: Accenture media client's subscriber-events 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 Accenture's utilities client's smart-meter reading feed does a groupBy on a billion rows. Where does the time go?

   HardPython and Spark roundshuffle and partitioningAccenture-specific

   Context: Accenture utilities client's smart-meter reading 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 Accenture's travel client's booking and cancellation mart, and when does that blow up?

   MediumPython and Spark roundbroadcast joinsAccenture-specific

   Context: Accenture travel client's booking and cancellation mart

   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 Accenture's banking client's nightly core-banking ingestion has hundreds of thousands of tiny files. What is the damage, and what is the fix?

    HardPython and Spark roundsmall files problemAccenture-specific

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

    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 Accenture's retail client's daily sales fact load with a Python UDF. When is that the wrong call?

    MediumPython and Spark roundUDFs vs built-insAccenture-specific

    Context: Accenture retail client's daily sales fact load

    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 Accenture's telecom client's call-detail-record pipeline is used four times. Do you cache it? Defend the answer.

    MediumPython and Spark roundcaching and reuseAccenture-specific

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

    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 Accenture's insurance client's claims data lake in one sentence, and show what breaks when the grain is wrong.

    MediumData modelling roundgrain definitionAccenture-specific

    Context: Accenture insurance client's claims data lake

    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 Accenture's e-commerce client's clickstream ingestion as a star schema. Which tables are facts, which are dimensions, and why not one wide table?

    MediumData modelling roundstar schema designAccenture-specific

    Context: Accenture e-commerce client's clickstream 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 Accenture's healthcare client's patient-records batch feed, and what is not allowed to happen in each.

    MediumData modelling roundlayered architectureAccenture-specific

    Context: Accenture healthcare client's patient-records batch 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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