Databricks interviews for data engineering and ML engineering roles in India test deep proficiency in Apache Spark, Delta Lake, and the Lakehouse architecture that Databricks pioneered.
In 2026, expect a coding screen on Python and SQL, followed by rounds on distributed data pipeline design, Spark optimisation (partitioning, skew, broadcast joins), Delta Lake ACID guarantees, and MLflow for experiment tracking. Databricks prizes engineers who understand the performance trade-offs of their own platform and can reason about cost-efficiency at petabyte scale.
About Databricks
Databricks India (Bengaluru) is a premium GCC for the Data + AI lakehouse platform, working on the Delta Lake engine, MLflow, Unity Catalog, and AI/BI (DBRX / Mosaic ML).
Apply or get sourced; recruiter screen and technical pre-screen
Online coding assessment in Python and SQL with data engineering problems
Technical interviews on Spark, Delta Lake, and distributed pipeline design
System design round on Lakehouse architecture, then hiring-manager and offer
Round 1 (45-60 min)
online coding screen with Python and SQL data manipulation problems at medium-hard LeetCode level.
Round 2 (60 min)
live technical interview on Spark internals, query optimisation, and Delta Lake operations with practical scenarios.
Round 3 (60-75 min)
distributed system design for a data pipeline: ingest, transform, store, and serve at petabyte scale on the Lakehouse architecture.
Round 4 (45 min)
behavioural and hiring-manager round on ownership, cross-functional collaboration, and handling ambiguity in data platform projects.
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Sign up free: unlock all questionsThe typical Databricks recruitment process has 4 stages: Apply or get sourced; recruiter screen and technical pre-screen → Online coding assessment in Python and SQL with data engineering problems → Technical interviews on Spark, Delta Lake, and distributed pipeline design → System design round on Lakehouse architecture, then hiring-manager and offer.
Databricks typically conducts 4 interview rounds: Round 1 (45-60 min): online coding screen with Python and SQL data manipulation problems at medium-hard LeetCode level.; Round 2 (60 min): live technical interview on Spark internals, query optimisation, and Delta Lake operations with practical scenarios.; Round 3 (60-75 min): distributed system design for a data pipeline: ingest, transform, store, and serve at petabyte scale on the Lakehouse architecture.; Round 4 (45 min): behavioural and hiring-manager round on ownership, cross-functional collaboration, and handling ambiguity in data platform projects..
HireStepX recommends the Lakehouse Depth framework for this type of interview: Demonstrate hands-on Spark and Delta Lake fluency, reason about partition strategies and shuffle costs, and connect every design choice to cost and latency at scale
To answer this question well, HireStepX recommends the Lakehouse Depth approach: Demonstrate hands-on Spark and Delta Lake fluency, reason about partition strategies and shuffle costs, and connect every design choice to cost and latency at scale Ground your answer in a specific real example from your own experience.
To answer this question well, HireStepX recommends the Lakehouse Depth approach: Demonstrate hands-on Spark and Delta Lake fluency, reason about partition strategies and shuffle costs, and connect every design choice to cost and latency at scale Ground your answer in a specific real example from your own experience.
To answer this question well, HireStepX recommends the Lakehouse Depth approach: Demonstrate hands-on Spark and Delta Lake fluency, reason about partition strategies and shuffle costs, and connect every design choice to cost and latency at scale Ground your answer in a specific real example from your own experience.
To answer this question well, HireStepX recommends the Lakehouse Depth approach: Demonstrate hands-on Spark and Delta Lake fluency, reason about partition strategies and shuffle costs, and connect every design choice to cost and latency at scale Ground your answer in a specific real example from your own experience.
To answer this question well, HireStepX recommends the Lakehouse Depth approach: Demonstrate hands-on Spark and Delta Lake fluency, reason about partition strategies and shuffle costs, and connect every design choice to cost and latency at scale Ground your answer in a specific real example from your own experience.