Databricks Big Data Engineer Interview Questions (2026)

1. 1Recruiter Screen30 minEasy

   Role calibration and an honest preview of the coding difficulty; sets expectations for the loop.

2. 2Coding Phone Screen60 minHard

   One LeetCode hard-leaning problem to complete, working code with edge cases handled - interviewer runs the code mentally or literally.

3. 3Onsite Coding I & II60 minHard

   Two more hard implementation rounds; problems often disguise systems concepts (LRU variants, schedulers, query planners) requiring airtight code.

4. 4Distributed System Design60 minHard

   Design a data-infrastructure system (distributed query engine, job scheduler, storage layer) with deep follow-ups on failure modes and data layout.

5. 5SQL & Data Engineering Round60 minHard

   For data/field roles: Spark/SQL optimization, partitioning strategy and pipeline debugging on realistic lakehouse scenarios.

6. 6Hiring Manager Round45 minMedium

   Project deep-dive doubling as the behavioral round - motivation, ownership, and technical judgment interrogated through your past work.

1. Q1

   For a Databricks-like data project, tell me about a time you missed a deadline. What did you do?

   MediumRound 8: BehavioralAccountability

   How to answer: Communicate early, reset scope or timeline, explain root cause, protect critical users, and improve planning afterward.

2. Q2

   Give an example of ambiguous requirements for a data product similar to Databricks's dashboards and downstream ML features. How did you clarify them?

   MediumRound 8: BehavioralAmbiguity

   How to answer: Identify users, decisions, metric definitions, freshness needs, edge cases, and acceptance criteria before building.

3. Q3

   At Databricks, data decisions often involve trade-offs. Tell me about a conflict with another engineer over Delta Lake analytics platform or Delta Lake, Spark, Lakeflow Jobs, Unity Catalog, and SQL Warehouses

   MediumRound 8: BehavioralConflict Resolution

   How to answer: State both positions fairly, explain evidence gathered, describe the decision process, and show the relationship stayed healthy.

4. Q4

   Design cost controls for Databricks's Delta Lake analytics platform where query and pipeline spend is growing faster than usage

   MediumRound 6: System DesignCost and Performance Design

   How to answer: Measure cost by owner and workload, optimize scans and files, right-size compute, cache or materialize common aggregates, and enforce budgets.

5. Q5

   Design an event contract and schema registry process for Databricks's lakehouse platform telemetry producers and data consumers

   MediumRound 6: System DesignData Contracts

   How to answer: Define versioned schemas, compatibility rules, ownership, validation at ingestion, documentation, and a migration process for breaking changes.

6. Q6

   Design observability for Databricks's critical job runs pipelines across freshness, quality, volume, and cost

   MediumRound 6: System DesignData Observability

   How to answer: Collect SLIs for freshness, completeness, validity, failure rate, latency, and spend; alert on symptoms and attach run-level lineage.

7. Q7

   Define data quality checks for Databricks's run_id, workspace_id, cluster_id, job_id, status, dbus, event_time pipeline before publishing to analysts

   MediumRound 5: ETL DesignData Quality

   How to answer: Check schema, nullability, uniqueness, referential integrity, volume anomalies, value ranges, freshness, and reconciliation against source totals.

8. Q8

   Design a Databricks data system for workspace telemetry and data platform observability with end-to-end latency of under 5 minutes

   HardRound 6: System DesignData System Design

   How to answer: Use durable event ingestion, streaming processing, curated storage, low-latency serving, monitoring, and replayable raw logs.

9. Q9

   Design a star schema for Databricks's lakehouse data and AI platform that supports reporting on successful job runs, DBU consumption, and trial -> workspace creation -> first job -> production workload

   MediumRound 4: Data ModelingDimensional Modeling

   How to answer: Define a clear fact grain for job runs, add conformed dimensions such as workspace, cluster, customer, and cloud-region dimensions, and store additive measures separately from derived metrics.

10. Q10

    Design a daily ETL pipeline for Databricks that ingests lakehouse platform telemetry into Delta Lake analytics platform for successful job runs reporting

    MediumRound 5: ETL DesignETL Architecture

    How to answer: Land raw data, validate schema, transform to curated tables, run data quality checks, publish aggregates, and monitor freshness and failures.

11. Q11

    A Databricks ETL job failed halfway after writing partial data. Walk through the recovery design

    HardRound 5: ETL DesignETL Failure Recovery

    How to answer: Detect partial output, roll back or overwrite the affected partition, rerun from checkpoint, validate row counts, and publish only after atomic completion.

12. Q12

    How would you make Databricks's Airflow DAG for job runs processing idempotent and safe to backfill?

    HardRound 5: ETL DesignETL Orchestration

    How to answer: Use deterministic input ranges, write to temporary paths, validate outputs, atomic swap/merge, and parameterize DAG runs by logical date.

13. Q13

    Design reconciliation logic for Databricks's ETL so DBU consumption in Delta Lake analytics platform matches the operational source

    MediumRound 5: ETL DesignETL Reconciliation

    How to answer: Compare control totals by date and cloud region, track accepted tolerances, investigate deltas, and block publishing on material mismatches.

14. Q14

    For Databricks, decide between ETL and ELT for transforming workspace, job, cluster, query, and model-serving events. What factors drive your choice?

    MediumRound 5: ETL DesignETL vs ELT

    How to answer: Choose ELT when the warehouse/lakehouse can scale transformations cheaply; choose ETL when privacy, bandwidth, or source constraints require pre-load shaping.

15. Q15

    For Databricks's privacy-sensitive data such as customer workspace path, tell me about a time you raised an ethics, privacy, or governance concern

    HardRound 8: BehavioralEthics and Privacy

    How to answer: Describe the concern, policy or risk, who you involved, the decision, and how the safer approach still met business needs.

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Know Spark/distributed data deeply; strong coding; prepare data-platform design

Indicative Big Data Engineer pay in India: ~₹8–35 LPA (role-level range, not a Databricks-specific figure).

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