Jobs at Databricks

10,578 open positions

Databricks is the creator of the Lakehouse platform, blending data warehousing and data lake capabilities on top of Apache Spark. Their flagship product, Delta Lake, lets enterprises store, process, and analyze petabyte‑scale data with ACID guarantees and real‑time streaming. The company serves finance, healthcare, retail, and AI research clients, positioning itself as a key player in cloud‑native data analytics.

Databricks hires across engineering, data science, platform architecture, security, product management, and business development. Candidates can expect rigorous technical interviews, a culture that rewards experimentation, and opportunities to work on high‑impact projects that power real‑world analytics for Fortune 500 customers. Remote and hybrid options are common, and many teams operate cross‑continent with clear career ladders.

Job Transparency’s listings provide exact salary ranges, average base pay, and employee sentiment for every Databricks role. With this data, applicants can benchmark offers, negotiate proactively, and verify that the company’s culture matches their professional goals before applying.

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Frequently Asked Questions

What is it like to work at Databricks?
Employees describe Databricks as a fast‑moving, data‑centric environment that encourages deep technical ownership. The company offers robust mentorship programs, regular hackathons, and a culture that rewards experimentation with new ML models and stream‑processing pipelines. Remote work is supported, but on‑site teams in Chicago, San Francisco, and Washington, D.C. meet weekly for cross‑functional syncs. Career growth is transparent, with documented promotion tracks and frequent performance reviews that focus on measurable impact.
What types of positions are available at Databricks?
Databricks posts roles in software engineering, infrastructure and platform engineering, data engineering, machine learning research, security engineering, product management, user experience design, marketing, sales, and customer success. Each role typically requires experience with Python, Scala, or Java, strong problem‑solving skills, and familiarity with cloud platforms such as AWS, Azure, or GCP. Data‑focused positions often involve building or optimizing Delta Lake workloads, while platform roles focus on expanding the Lakehouse API and improving performance at scale.
How can I stand out as an applicant for Databricks?
Showcase hands‑on experience with Spark or Delta Lake by sharing GitHub repositories or open‑source contributions. Prepare for technical interviews by mastering distributed systems concepts, algorithm design, and coding in your chosen language. Highlight any real‑world data‑engineering projects where you improved performance or reliability at scale. During the interview, demonstrate curiosity by asking about the company’s roadmap for Lakehouse, and convey how you can accelerate that vision with your unique skill set.

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