Databricks Data Engineer Interview Questions (2026)
The 15 Data Engineer interview questions most worth practising for Databricks, selected from a bank of 238. Design and operate scalable data pipelines and platforms powering analytics and ML. Below: the interview process, the questions with answer outlines, the topics tested, and how to prepare.
Databricks is notorious for one of the hardest pure-coding bars in the industry: phone screens and onsite coding rounds regularly use LeetCode-hard problems demanding fully working, tested code, followed by deep distributed-systems design given its Spark heritage. The Bengaluru R&D office holds the same bar as San Francisco, and many strong candidates fail on speed-to-correct-code.
Questions
15
from a 238-question bank
Difficulty
Medium
from our question mix
Rounds
6
typical loop
Role
Data Engineer
interview prep
Databricks's interview process
- 1Recruiter Screen30 minEasy
Role calibration and an honest preview of the coding difficulty; sets expectations for the loop.
- 2Coding Phone Screen60 minHard
One LeetCode hard-leaning problem to complete, working code with edge cases handled - interviewer runs the code mentally or literally.
- 3Onsite Coding I & II60 minHard
Two more hard implementation rounds; problems often disguise systems concepts (LRU variants, schedulers, query planners) requiring airtight code.
- 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.
- 5SQL & Data Engineering Round60 minHard
For data/field roles: Spark/SQL optimization, partitioning strategy and pipeline debugging on realistic lakehouse scenarios.
- 6Hiring Manager Round45 minMedium
Project deep-dive doubling as the behavioral round - motivation, ownership, and technical judgment interrogated through your past work.
Data Engineer interview questions for the Databricks loop
- Q1
Design an event contract and schema registry process for Databricks's lakehouse platform telemetry producers and data consumers
MediumRound 6: System DesignData ContractsHow to answer:Define versioned schemas, compatibility rules, ownership, validation at ingestion, documentation, and a migration process for breaking changes.
- Q2
Model Databricks's job run analytics using facts and dimensions. What is the fact grain?
MediumRound 2: Product Telemetry Data ModelingData ModelingHow to answer:The fact grain should be one row per business-defined job run unless a lifecycle-event grain is explicitly needed. Dimensions such as cloud provider, workspace tier, and user attributes should attach through stable keys. The explanation should emphasize declaring grain first, because metric correctness and join safety depend on it.
- Q3
Which conformed dimensions would help Databricks compare DBU consumption, job failure rate, and lakehouse adoption across teams?
MediumRound 2: Product Telemetry Data ModelingData ModelingHow to answer:Common dimensions such as date, cloud provider, workspace tier, and user or account segments should be standardized across marts. Conformed dimensions reduce reconciliation debates because teams slice metrics the same way. The answer should mention ownership, slowly changing attributes, and backward compatibility.
- Q4
When would you use a periodic snapshot fact for Databricks's system usage telemetry?
HardRound 2: Product Telemetry Data ModelingData ModelingHow to answer:Use a periodic snapshot when the business needs state at regular intervals, such as daily availability, balance, utilization, or inventory. It complements event facts by making point-in-time reporting easier. The explanation should include snapshot grain, date spine, storage tradeoffs, and handling corrections.
- Q5
How would you handle facts that arrive before their workspace tier dimension row at Databricks?
HardRound 2: Product Telemetry Data ModelingData ModelingHow to answer:Load the fact with an unknown or inferred dimension key, then update the surrogate key when the dimension arrives. Preserve the natural key so reconciliation is possible. The explanation should discuss the tradeoff between delaying facts for completeness and publishing timely but partially attributed data.
- Q6
How would you model data so weekly active workspaces, query success rate, and job failure rate are easy to define consistently at Databricks?
HardRound 2: Product Telemetry Data ModelingData ModelingHow to answer:Create facts with additive numerator and denominator columns, clear eligibility flags, and conformed dimensions. Ratio metrics should be calculated from sums rather than stored averages. The answer should include grain, exclusions, status logic, and tests that prevent duplicate denominators.
- Q7
What retention strategy would you apply to raw cluster start data and curated job run marts at Databricks?
MediumRound 7: Lakehouse Warehousing ArchitectureData WarehousingHow to answer:Keep raw data long enough for audit, replay, and compliance needs, but consider tiered storage or aggregation for very high-volume events. Curated marts often need longer retention because they support trends and finance or product history. The answer should account for privacy deletion and legal requirements.
- Q8
How would you orchestrate dependencies for Databricks's warehouse models that compute lakehouse adoption?
MediumRound 7: Lakehouse Warehousing ArchitectureData WarehousingHow to answer:Schedule ingestion, transformation, tests, and publication according to dependency order, with retries and clear failure states. Critical models should block downstream dashboards when tests fail, while noncritical warnings can be surfaced separately. The explanation should mention lineage-aware orchestration and backfill support.
- Q9
A Databricks dashboard on query success rate times out every morning. How would you debug the warehouse side?
HardRound 7: Lakehouse Warehousing ArchitectureData WarehousingHow to answer:Inspect query history, execution plans, scanned bytes, join cardinality, and concurrent workload. Then tune partitions, clustering, materialization, and aggregate tables. The answer should include checking whether the BI tool generates inefficient SQL or bypasses cached/certified marts.
- Q10
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 ModelingHow 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.
- Q11
Design reconciliation logic for Databricks's ETL so DBU consumption in Delta Lake analytics platform matches the operational source
MediumRound 5: ETL DesignETL ReconciliationHow to answer:Compare control totals by date and cloud region, track accepted tolerances, investigate deltas, and block publishing on material mismatches.
- Q12
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 ELTHow to answer:Choose ELT when the warehouse/lakehouse can scale transformations cheaply; choose ETL when privacy, bandwidth, or source constraints require pre-load shaping.
- Q13
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 PrivacyHow to answer:Describe the concern, policy or risk, who you involved, the decision, and how the safer approach still met business needs.
- Q14
How would you design a long-term holdout for Databricks's product changes affecting job failure rate or DBU consumption?
HardRound 6: Platform Onboarding Experimentation CaseExperimentationHow to answer:Keep a stable control group excluded from a class of changes to estimate cumulative long-term impact. The holdout must be large enough, ethically acceptable, and protected from contamination. The explanation should mention governance, opportunity cost, and how holdouts differ from short A/B tests.
- Q15
How would you model Databricks's serverless SQL warehouse onboarding experiment results so analysts can compare treatment and control without metric leakage?
HardRound 4: Data ModelingExperimentation ModelingHow to answer:Create assignment facts at exposure time, immutable variant dimensions, and outcome facts joined by actor and valid time windows.
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Topics tested most
How to prepare for the Databricks Data Engineer interview
Know Spark/distributed data deeply; strong coding; prepare data-platform design
Indicative Data Engineer pay in India: ~₹10–45 LPA (role-level range, not a Databricks-specific figure).
Frequently asked questions
How hard is the Databricks Data Engineer interview?
Based on our 238-question Data Engineer bank for the Databricks loop, the overall difficulty is medium (Databricks's process is generally rated extreme). Expect around 6 rounds spanning Data Modeling, Data Warehousing, Experimentation.
How many interview rounds does Databricks have for a Data Engineer?
Databricks typically runs about 6 rounds for Data Engineer candidates: Recruiter Screen → Coding Phone Screen → Onsite Coding I & II → Distributed System Design → SQL & Data Engineering Round.
What is the interview process at Databricks?
The Databricks interview process typically runs: Recruiter screen -> technical screen -> onsite (coding, distributed-systems/data design, domain depth, behavioral). Prepare for each round in order rather than only the first — the later stages usually carry the most weight.
How hard is the Databricks interview?
Databricks interviews are rated very high difficulty. The bar is highest on data engineering & distributed systems — go deep there and practise explaining your reasoning out loud.
What does Databricks look for in candidates?
Databricks focuses on Data engineering & distributed systems, Spark/lakehouse depth, coding. Culturally, it values Customer obsession, raise the bar, truth-seeking, ownership. Line up your examples to hit both the technical bar and these values.
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Compiled by PrepNPlaced from 238+ interview reports and question banks for the Databricks Data Engineer loop. Updated 2026.