Staff Software Engineer - Distributed Data Systems

About the Role Staff Software Engineer on the Runtime team at Databricks will help build the next generation distributed data storage and processing systems that scale to exabytes of data and power diverse workloads from ETL to data science. You will contribute to the core infrastructure that underpins our data lakehouse platform and deliver high-performance, fault-tolerant systems.

What You'll Do

• Design, implement, and optimize distributed data storage and processing components.
• Build and improve storage backends and client libraries for cloud storage backends (e.g., AWS S3, Azure Blob Store).
• Contribute to Delta Lake features (ACID transactions, time travel) and Delta Pipelines to orchestrate thousands of pipelines.
• Develop and optimize the query optimizer and execution engine for fast, scalable relational query performance.
• Collaborate across teams to ship reliable, secure, and scalable data infrastructure at Databricks scale.
• Troubleshoot production issues and ensure reliability and resilience of the system.

What We're Looking For

• BS in Computer Science, related technical field or equivalent practical experience. Optional: MS or PhD in databases, distributed systems.
• 8+ years of production-level experience in either Java, Scala or C++.
• Strong foundation in algorithms and data structures and their real-world use cases.
• Experience with distributed systems, databases, and big data systems (Apache Spark, Hadoop).
• Comfortable working toward a multi-year vision with incremental deliverables and a customer-value mindset.

Nice to Have

• MS or PhD in databases or distributed systems (optional).
• Experience with Apache Spark and Hadoop in production settings.
• Familiarity with cloud storage backends (S3, Azure) and data lakehouse architectures.

Compensation & Benefits

• Local Pay Range: USD 192,000—260,000 per year. The total compensation package may include annual performance bonus, equity, and the benefits listed above.
• Benefits are region-specific; see the provided link for details.