Staff Software Engineer - Multi Cloud Efficiency
Databricks is a data and AI platform that lets organizations build analytics, AI agents, and applications on a unified, governed lakehouse.
Maintainer signals as of 9/23/2026
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 serve as the technical lead for cloud-efficiency projects. You will design systems for cloud-spend optimization, cost attribution, resource governance, regression detection, and rightsizing. You will lead technical strategy, resolve system-wide performance issues, build autonomous efficiency automation, and mentor engineers.
Requirements
- 9+ years of experience building production-grade distributed systems
- Experience with Java, Scala, C++, or Go
- Experience architecting large-scale infrastructure
- Cloud computing experience with AWS, Azure, or GCP
- Container orchestration experience with Kubernetes or Docker
- Expertise identifying and solving performance bottlenecks in distributed environments
- Ability to drive technical requirements for ambiguous problems
- Distributed systems, cloud infrastructure, monitoring, and software lifecycle knowledge
Responsibilities
- Lead the design and implementation of cloud-spend optimization systems
- Architect reserved-instance automation, cost attribution, waste-cleanup policies, and resource governance
- Define the efficiency roadmap and conduct design reviews and risk assessments
- Design systems for regression detection and automated resource rightsizing
- Build efficiency-footprint measurement frameworks and release gates
- Address complex system-wide performance issues
- Improve the stability and reliability of internal platform tools
- Mentor engineers and raise engineering quality
- Design AI-driven detection, decisioning, and autonomous remediation systems