Sr. Forward Deployed Engineer

Databricks•Washington, DC

3d•$182,000 - $250,208

About The Position

As a Sr. Forward Deployed Engineer, you will work with federal, state, and local government customers to build and productionize solutions to their data and AI challenges using the Databricks platform. You will own architecture, lead design decisions, and implement end-to-end systems across data engineering, AI, and application development. You will collaborate with engineering, product, and developer relations teams to shape strategic priorities, while integrating with client systems, training customers, and helping them get value from their data. This is a hands-on, customer-facing, billable role for an adaptable builder who combines engineering expertise with customer empathy and delivers projects to specification and measurable outcomes. The role involves leading impactful technical projects and transformational big data initiatives, guiding customers through architecture, design, implementation, evaluation, and adoption of Databricks, and ensuring solutions are secure, scalable, and aligned with customer needs and Databricks best practices. You will work with Databricks technical teams, project managers, architects, and customer teams to deliver technical components, provide implementation feedback, and help resolve project-specific product and support issues. You will embed with customer teams and engage stakeholders ranging from individual contributors to executives. You will also contribute reusable accelerators, frameworks, and best practices that scale impact across accounts and inform the Databricks product roadmap. The role requires 25% travel to client sites. Eligibility to obtain a U.S. government Public Trust, Secret, and/or Top Secret clearance is required to access classified information; candidates interested in and eligible to obtain an active Public Trust or higher clearance are encouraged to apply.

Requirements

At least 5 years of experience in data engineering, data platforms and analytics, or software engineering.
Fluency writing code in Python, Scala, JavaScript/TypeScript, and modern frameworks.
Working knowledge of at least two common cloud ecosystems (AWS, Azure, or GCP), with expertise in at least one.
Deep experience with distributed computing using Apache Spark and knowledge of Spark runtime internals.
Familiarity with CI/CD for production deployments.
Working knowledge of MLOps, ML/AI models, and AI APIs.
Experience designing and deploying performant, production end-to-end data architectures and applications combining data pipelines, ML/AI models, and user-facing interfaces.
Experience with technical project delivery, documentation, and whiteboarding.
Experience working with enterprise clients and managing conflicts across a broad range of stakeholders.
Eligibility to obtain a U.S. government Public Trust, Secret, and/or Top Secret clearance is required to access classified information.

Nice To Haves

At least 8 years of experience in data engineering, data platforms and analytics, or software engineering.
Databricks Certification.
Expertise in MLOps, ML/AI models, and AI APIs.

Responsibilities

Lead customer technical projects, delivering production-grade systems, reference architectures, custom applications, data ingestion, and ML/AI model integration.
Guide strategic customers through end-to-end design, development, and deployment of big data and AI applications.
Work with engagement managers and customers to scope technical delivery work.
Guide customers on architecture and design, and bootstrap or implement projects to support their understanding, evaluation, and adoption of Databricks.
Own architecture and design decisions, ensuring solutions are secure, scalable, and aligned with customer needs and Databricks best practices.
Coordinate with Databricks technical teams, project managers, architects, and customer teams to deliver technical components that meet customer needs.
Collaborate with engineers and customer support to provide product and implementation feedback and help resolve engagement-specific product and support issues.
Embed with customer teams and engage stakeholders from technical individual contributors to executives to understand challenges and deliver impact.
Contribute reusable accelerators, frameworks, and best practices that scale impact across accounts and influence the Databricks product roadmap.