Staff Software Engineer - Backend
Databricks is a data and AI platform that lets organizations build analytics, AI agents, and applications on a unified, governed lakehouse.
Funding history
Investors
About Databricks
Data engineers, analysts, and AI teams use Databricks to process large datasets, build reliable pipelines, and train models on a single governed platform. Users can run SQL analytics, serve ML predictions in real time, and deploy AI agents grounded in enterprise data. Its open lakehouse architecture provides consistent security and governance across analytical and operational workloads.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
You will build foundational infrastructure that operates across regions and cloud providers. You will implement cloud-agnostic abstractions, develop engineering productivity tools and processes, make architectural decisions, and mentor engineers through complex projects.
Requirements
- 8+ years of professional software development experience
- Bachelor’s degree or higher in Computer Science, a related field, or equivalent experience
- Proficiency in Java, Scala, Go, or another backend language
- Experience developing and operating large-scale critical distributed backend systems
- Leadership across project inception, design, implementation, and operations
- Experience mentoring engineers and influencing best practices
- Written and verbal communication skills
- Experience with infrastructure systems, security, sensitive data, Kubernetes, or cloud platforms such as AWS, Azure, or GCP
Responsibilities
- Build foundational infrastructure platforms across geographic regions and cloud providers
- Implement cloud-agnostic infrastructure abstractions
- Develop tools and processes that improve engineering efficiency
- Optimize the Rust development experience
- Drive architectural decisions through project design, implementation, and operations
- Mentor engineers and influence engineering best practices