Staff Forward Deployed Engineer
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 work with customers to build and productionize solutions to their data and AI challenges on the Databricks platform. You will own the architecture, lead design decisions, and implement end-to-end systems spanning data engineering, AI, and application development. This is a hands-on, customer-facing and billable role with about 20% travel to customers.
Requirements
- 6+ years in data engineering, data platforms & analytics, or software engineering
- Comfortable writing code in Python, Scala, or JavaScript/TypeScript
- Working knowledge of two or more cloud ecosystems with expertise in one
- Deep experience with Apache Spark and Spark runtime internals
- Familiarity with CI/CD
- Working knowledge of MLOps, ML/AI models and AI APIs
- Technical project delivery experience
- Documentation and white-boarding skills
- Experience with enterprise clients
- Travel to customers 20% of the time
Responsibilities
- Deliver production-grade systems, reference architectures, custom applications, data ingestion and ML/AI model integration
- Guide strategic customers through big data projects from design to deployment
- Guide customers on architecture and design
- Lead architecture and design decisions
- Work with technical team, Project Manager, Architect and customer team
- Provide product and implementation feedback to Engineering and Customer Support
- Embed with customer teams and engage stakeholders
- Contribute accelerators, frameworks and best practices