Forward Deployed Engineer

India · Karnataka · 6+ years experience · Remote · Posted Oct 9, 2026

At a glance

Databricks is the Data and AI company. More than 20,000 organizations worldwide — including adidas, AT&T, Bayer, Block, Mastercard, Rivian, Unilever, and 70% of the Fortune 500 — rely on the Databricks Data + AI Platform to build and scale data and AI apps, analytics and agents. Headquartered in San Francisco with 30+ offices around the globe, Databricks offers a unified platform that includes Genie, Lakebase, Agent Bricks, Lakeflow, Lakehouse, and Unity Catalog. To learn more, follow Databricks on LinkedIn, X, YouTube, and Instagram.

The Forward Deployed Engineer (FDE) works directly with customers to build and productionize solutions to their data and AI challenges using the Databricks platform.
This role involves owning the architecture, leading design decisions, and implementing end-to-end systems that span data engineering, AI, and application development.
FDEs operate in a hands-on, customer-facing capacity to drive measurable outcomes and strategic technical initiatives.

Lead impactful customer technical projects by delivering production-grade systems and reference architectures.
Design, build, and deploy big data and AI applications.
Guide customers on architecture and design to facilitate successful adoption of Databricks.
Ensure solutions are secure, scalable, and aligned with Databricks best practices.
Collaborate with project managers, architects, and customer teams to meet engagement needs.
Provide product and implementation feedback to Engineering and Customer Support teams.
Embed with customer teams to understand challenges and deliver impact.
Contribute accelerators, frameworks, and best practices to scale impact across accounts.
Manage technical project delivery, including scope, timelines, and measurable outcomes.

6+ years of experience in data engineering, data platforms & analytics, or software engineering.
Proficiency in Python, Scala, or JavaScript/TypeScript.
Working knowledge of at least two common Cloud ecosystems (AWS, Azure, GCP) with expertise in 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.
Experience with technical project delivery and managing enterprise client stakeholders.
Strong documentation and white-boarding skills.
Ability to travel to customer sites 20% of the time.