Databricks
Forward Deployed Engineer (FDE) - Public Sector
About this role
A customer-facing engineering role at Databricks where you'll design and deploy production data and AI solutions on the Databricks platform for public sector clients. You'll lead end-to-end technical projects, embed with customer teams, and drive strategic impact through architecture decisions and hands-on implementation.
What you'll do
- Design, build, and deploy production-grade data engineering and ML/AI systems using Databricks
- Lead architecture and design decisions ensuring solutions are secure, scalable, and follow best practices
- Embed with customer teams to understand challenges and deliver transformational big data projects
- Manage technical project scope, timelines, and measurable outcomes with enterprise stakeholders
- Contribute accelerators, frameworks, and best practices that influence product roadmap
- Coordinate with engineering, support, and account teams to resolve issues and gather product feedback
What they're looking for
- Data engineering and distributed computing with Apache Spark
- Python, Scala, JavaScript/TypeScript, or modern frameworks
- Cloud platforms (AWS, Azure, GCP) with deep expertise in at least one
- MLOps and ML/AI model deployment and integration
- CI/CD and production deployment practices
- End-to-end data architecture design and application development
- Technical project management and stakeholder communication
- Enterprise client engagement and technical documentation
Opens the official application on the employer’s site. No login required.
Databricks
Databricks builds a unified data and AI platform that combines database systems, distributed computing, and generative AI capabilities across multi-cloud infrastructure. The company is hiring software engineers, applied AI engineers, and web engineers to develop core database engines, ML/AI features, inference systems, and user-facing products.
- Website
- databricks.com
Likely interview questions
- Describe a complex data architecture you designed end-to-end—what were the key decisions and tradeoffs you made?
- How have you approached learning and working with Databricks specifically, or similar distributed data platforms?