Databricks Big Data Engineer Interview Questions (2026)
The 15 Big Data Engineer interview questions most worth practising for Databricks, selected from a bank of 72. Build distributed pipelines that ingest, process, and store terabytes of data reliably at scale. 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 72-question bank
Difficulty
Medium
from our question mix
Rounds
6
typical loop
Role
Big 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.
Big Data Engineer interview questions for the Databricks loop
- Q1
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.
- Q2
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.
- Q3
Design observability for Databricks's critical job runs pipelines across freshness, quality, volume, and cost
MediumRound 6: System DesignData ObservabilityHow to answer:Collect SLIs for freshness, completeness, validity, failure rate, latency, and spend; alert on symptoms and attach run-level lineage.
- Q4
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 QualityHow to answer:Check schema, nullability, uniqueness, referential integrity, volume anomalies, value ranges, freshness, and reconciliation against source totals.
- Q5
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 DesignHow to answer:Use durable event ingestion, streaming processing, curated storage, low-latency serving, monitoring, and replayable raw logs.
- Q6
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 ArchitectureHow to answer:Land raw data, validate schema, transform to curated tables, run data quality checks, publish aggregates, and monitor freshness and failures.
- Q7
How would you make Databricks's Airflow DAG for job runs processing idempotent and safe to backfill?
HardRound 5: ETL DesignETL OrchestrationHow to answer:Use deterministic input ranges, write to temporary paths, validate outputs, atomic swap/merge, and parameterize DAG runs by logical date.
- Q8
For Databricks, would you model workspace, job, cluster, query, and model-serving events as an event fact table, an accumulating snapshot, or a periodic snapshot? Defend the choice
HardRound 4: Data ModelingFact Table DesignHow to answer:Use event facts for immutable actions, accumulating snapshots for lifecycle progress, and periodic snapshots for state at regular intervals.
- Q9
Design access control for Databricks's analytics platform where customer workspace path must be protected but aggregated analysis is allowed
HardRound 6: System DesignGovernance and SecurityHow to answer:Use role-based and attribute-based controls, row/column masking, tokenization, governed joins, audit logs, and least-privilege access.
- Q10
For Databricks's job runs data, design an incremental load using CDC or high-watermark logic
HardRound 5: ETL DesignIncremental ETLHow to answer:Capture changes since the last checkpoint, deduplicate, handle deletes and updates, merge into curated tables, and persist checkpoints transactionally.
- Q11
How would you add lineage and auditability to Databricks's job runs ETL pipeline?
MediumRound 5: ETL DesignLineage and AuditHow to answer:Capture source version, run ID, code version, input/output counts, checksums, timestamps, and upstream/downstream table dependencies.
- Q12
In Databricks's lakehouse data and AI platform, design a bridge table for many-to-many relationships between data teams and cloud accounts
HardRound 4: Data ModelingMany-to-Many ModelingHow to answer:Create a bridge table with surrogate relationship keys, effective dates when needed, allocation weights if measures must be split, and referential integrity checks.
- Q13
Why Databricks, and how does your experience map to technical depth, customer impact, open lakehouse thinking, and ownership?
EasyRound 7: Hiring ManagerMotivation and FitHow to answer:Connect specific company problems to your past work, show motivation, and give examples that demonstrate the stated values.
- Q14
Choose partitioning and clustering keys for Databricks's fact_job_runs to support common queries by time and cloud region
MediumRound 4: Data ModelingPhysical Data ModelingHow to answer:Partition primarily by event date, cluster or sort by cloud region and high-value join/filter keys, and avoid high-cardinality partitions.
- Q15
How would you prioritize between reducing Databricks's pipeline cost, improving freshness, and adding a new DBU consumption feature?
MediumRound 7: Hiring ManagerPrioritizationHow to answer:Estimate business value, risk, user impact, effort, and reversibility; align stakeholders on a ranked roadmap.
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Topics tested most
How to prepare for the Databricks Big Data Engineer interview
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).
Frequently asked questions
How hard is the Databricks Big Data Engineer interview?
Based on our 72-question Big Data Engineer bank for the Databricks loop, the overall difficulty is medium (Databricks's process is generally rated extreme). Expect around 6 rounds spanning Accountability, Ambiguity, Conflict Resolution.
How many interview rounds does Databricks have for a Big Data Engineer?
Databricks typically runs about 6 rounds for Big 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 72+ interview reports and question banks for the Databricks Big Data Engineer loop. Updated 2026.