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Data & Cloud Platforms

Databricks

Data + AI platform (Lakehouse) · Data Engineering interview

They build Spark — so expect deep Spark/Delta/medallion internals, a hard concurrent-programming round, and trade-off-driven design.

Cloud / platform

Multi-cloud; Spark, Delta Lake, Unity Catalog, Auto Loader, Workflows

Reported difficulty

Not reported

SQLPySparkSparkDelta LakeUnity Catalog

Hiring process

Recruiter

Technical phone

Virtual onsite (4–5h)

System design

Behavioral

Offer

Round-by-round breakdown

Technical phone

Medium confidence

SQL queries, Spark optimization, ETL design
Data modeling, schema evolution, performance tuning for large datasets

Coding / Algorithm (×2)

Medium confidence

DS/algo (LeetCode medium); Python

Concurrent programming

· reported "extremely hard"Medium confidence

Threads/locks/parallelism

Spark

· centralHigh confidence

Spark internals; Delta Lake architecture
Medallion (bronze/silver/gold); Unity Catalog; Auto Loader
Performance tuning (Z-order, OPTIMIZE/VACUUM)

System & pipeline design prompts

Lakehouse/medallion design; batch vs streaming, normalized vs denormalized, cost vs performance trade-offs

Prep — high priority

Spark internals + Delta + medallion + Auto Loader + Unity Catalog + tuning
SQL complex queries
Algo + concurrent programming; system-design trade-offs

Prep — medium priority

Data modeling/warehousing
Orchestration (Workflows/Jobs)
Behavioral impact

Rejection patterns

Cannot articulate system-design trade-offs (batch vs streaming, cost vs perf)
The concurrent-programming round (hard)
Shallow Spark internals at the company that builds Spark

Reported experiences

Data Engineer

IllustrativeComposite

Composite: recruiter (Spark/Delta depth) → phone (SQL + Spark optimization + modeling) → onsite (2 algo, 1 concurrent programming [hard], 1 behavioral, system design medallion/lakehouse with trade-offs).

Rehearse for Databricks

Answered questions on what Databricks actually asks — each with the short answer, the answer that gets you rejected, and the follow-up.

Then build one and have it reviewed: design a daily orders pipeline or model a marketplace. No account.

All interview experiences

Openingloading the content…

Data & Cloud Platforms

Databricks

Data + AI platform (Lakehouse) · Data Engineering interview

They build Spark — so expect deep Spark/Delta/medallion internals, a hard concurrent-programming round, and trade-off-driven design.

Cloud / platform

Multi-cloud; Spark, Delta Lake, Unity Catalog, Auto Loader, Workflows

Reported difficulty

Not reported

SQLPySparkSparkDelta LakeUnity Catalog

Hiring process

Recruiter

Technical phone

Virtual onsite (4–5h)

System design

Behavioral

Offer

Round-by-round breakdown

Technical phone

Medium confidence

SQL queries, Spark optimization, ETL design
Data modeling, schema evolution, performance tuning for large datasets

Coding / Algorithm (×2)

Medium confidence

DS/algo (LeetCode medium); Python

Concurrent programming

· reported "extremely hard"Medium confidence

Threads/locks/parallelism

Spark

· centralHigh confidence

Spark internals; Delta Lake architecture
Medallion (bronze/silver/gold); Unity Catalog; Auto Loader
Performance tuning (Z-order, OPTIMIZE/VACUUM)

System & pipeline design prompts

Lakehouse/medallion design; batch vs streaming, normalized vs denormalized, cost vs performance trade-offs

Prep — high priority

Spark internals + Delta + medallion + Auto Loader + Unity Catalog + tuning
SQL complex queries
Algo + concurrent programming; system-design trade-offs

Prep — medium priority

Data modeling/warehousing
Orchestration (Workflows/Jobs)
Behavioral impact

Rejection patterns

Cannot articulate system-design trade-offs (batch vs streaming, cost vs perf)
The concurrent-programming round (hard)
Shallow Spark internals at the company that builds Spark

Reported experiences

Data Engineer

IllustrativeComposite

Rehearse for Databricks

Answered questions on what Databricks actually asks — each with the short answer, the answer that gets you rejected, and the follow-up.

Then build one and have it reviewed: design a daily orders pipeline or model a marketplace. No account.

All interview experiences