Databricks Data Engineer Interview Questions (2026)
238 real Data Engineer interview questions compiled for Databricks. 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
238
0 company-tailored
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 asked at Databricks
- Q1
For a Databricks-like data project, tell me about a time you missed a deadline. What did you do?
MediumRound 8: BehavioralAccountabilityHow to answer: Communicate early, reset scope or timeline, explain root cause, protect critical users, and improve planning afterward.
- 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: BehavioralAmbiguityHow to answer: Identify users, decisions, metric definitions, freshness needs, edge cases, and acceptance criteria before building.
- 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 ResolutionHow to answer: State both positions fairly, explain evidence gathered, describe the decision process, and show the relationship stayed healthy.
- 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 DesignHow to answer: Measure cost by owner and workload, optimize scans and files, right-size compute, cache or materialize common aggregates, and enforce budgets.
- Q5
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.
- Q6
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.
- Q7
How would you model cluster start, notebook run, SQL query execution, and Delta table write events for Databricks?
MediumRound 2: Product Telemetry Data ModelingData ModelingHow to answer: Keep a raw immutable event table, create typed staging models per event family, and build a lifecycle or funnel fact for analysis. The raw layer supports audit and replay, while analytics models standardize timestamps, identities, and event names. Good answers address schema evolution and duplicate events.
- Q8
workspace tier attributes change over time at Databricks. Design the dimension table
HardRound 2: Product Telemetry Data ModelingData ModelingHow to answer: Use an SCD Type 2 dimension with surrogate key, natural key, effective_start, effective_end, is_current, and tracked attributes. Facts should join to the dimension version valid at the fact timestamp. This allows historical analysis without overwriting old attributes when current-state source values change.
- Q9
Databricks needs to analyze job runs by multiple classifications of workspace tier. When would you use a bridge table?
HardRound 2: Product Telemetry Data ModelingData ModelingHow to answer: Use a bridge table when one job run or dimension member can belong to multiple categories and a simple foreign key would force duplication. The bridge should include allocation weights if metrics need to sum correctly across classifications. The explanation should warn about double counting and clearly define how rollups are handled.
- Q10
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.
- Q11
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.
- Q12
Design an accumulating snapshot for the lifecycle from SQL query execution to Delta table write to job completion at Databricks
HardRound 2: Product Telemetry Data ModelingData ModelingHow to answer: Create one row per job run with milestone timestamps and statuses that update as the lifecycle progresses. This makes duration and bottleneck metrics easy to calculate. The answer should discuss mutable records, idempotent updates, late-arriving milestones, and whether completed records become immutable.
- Q13
Databricks has anonymous events and logged-in events for workspace admins. How would you model identity resolution?
SeniorRound 2: Product Telemetry Data ModelingData ModelingHow to answer: Maintain an identity graph that maps anonymous IDs, device IDs, and user IDs with validity windows and confidence levels. Analytics models should choose the identity appropriate to the metric, such as session-level or account-level. The explanation should address privacy, merge/split corrections, and reproducibility of historical metrics.
- Q14
How would you model adjustments such as delayed cloud billing reconciliation so Databricks's lakehouse adoption remains reconcilable?
HardRound 2: Product Telemetry Data ModelingData ModelingHow to answer: Use an append-only ledger or adjustment fact with transaction type, effective date, posted date, amount, currency, and source reference. Avoid overwriting historical amounts without an audit trail. The explanation should separate operational correction time from business effective time and show how net metrics are derived.
- Q15
Give a factless fact example for Databricks involving cluster start or eligibility
MediumRound 2: Product Telemetry Data ModelingData ModelingHow to answer: A factless fact can record that a user, account, or item was eligible for an experience, policy, or inventory state even when no measurable transaction occurred. It supports denominator calculations and coverage analysis. The explanation should mention that absence of activity is meaningful only when the eligible population is modeled.
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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 bank of 238 Data Engineer questions asked at Databricks, 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.