What Databricks actually looks for

Databricks was founded by the original creators of Apache Spark, and that open-source, research-driven DNA still shows: the company has released Delta Lake, MLflow, and other widely-used open tools alongside its commercial lakehouse platform, which combines data warehousing and AI or ML workloads in one system instead of keeping them in separate tools. Interviewers often want to see genuine intellectual curiosity about data and machine learning infrastructure, not just familiarity with Databricks as a product.

Common questions and how to answer them

"What is a lakehouse and why would a company choose it over a separate data warehouse and data lake?" This is Databricks' core architectural pitch, so a clear, accurate explanation shows real preparation. "Tell me about a project where you had to work across data engineering and data science or machine learning." Databricks' whole platform exists to unify these functions, so a real example of bridging that gap is directly relevant. "How do you evaluate whether to build on open standards versus a proprietary approach?" Databricks balances open-source contributions with a proprietary commercial platform, so this tests understanding of that tension.

How to prepare

Understand the lakehouse concept and how it differs from a traditional data warehouse or data lake, since this is the central technical idea behind Databricks' entire product. If you have any hands-on experience with Spark, Delta Lake, or MLflow, be ready to discuss it specifically, genuine familiarity stands out against candidates who only know the company by name.

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Frequently asked questions

Do I need machine learning experience to work at Databricks?
It depends on the role, engineering roles tied to the ML platform benefit from it, but Databricks also has large data engineering, sales, and go-to-market organizations that don't require deep ML expertise.
What's the difference between Databricks and Snowflake?
Both are cloud data platforms, but Databricks grew from a data engineering and machine learning foundation, the lakehouse, while Snowflake grew from data warehousing, though the two have increasingly expanded into each other's territory.

Before your next interview, it helps to have the fundamentals down. Our complete guide to preparing for a job interview covers the basics, and the STAR method is a reliable way to structure almost any answer under pressure.

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