Forward Deployed Engineering: Delivering Business Outcomes with AI

Forward Deployed Engineering: Delivering Business Outcomes with AI | Databricks Blog

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June 11, 2026

Forward Deployed Engineering: Delivering Business Outcomes with AI

Databricks is launching Forward Deployed Engineering to accelerate customer data and AI outcomes.

by Jason Martin

Summary

• Databricks is launching its Forward Deployed Engineering (FDE) org to accelerate customer business outcomes with AI. It pairs the Lakehouse platform (Apps, Genie, Lakebase) with embedded, engineering-led delivery, a global partner network, and direct R&D interlock.

• Customers have moved from "help us migrate and build pipelines" to "help us solve our business problem." FDE spans the full arc from migration to production AI agents, replacing consultant-style handoffs with engineers who build what doesn't yet exist, anchored on shared OKRs and customer outcomes.

• Results: 1,900+ customers in 12 months. Fox doubled its search success rate (a quarter of search traffic now from a co-built feature, serving hundreds of thousands of daily requests through live events); JPMC migrated 5+ petabytes and 500+ notebooks in four months and trained 600+ users; Qualcomm shifted from isolated AI experiments to a production-grade agentic model, cutting multi-day workflows to minutes.

Over the past few years, increasingly customers have shifted from asking “help us migrate and build data pipelines” to “help us solve our business problem.” That transition from infrastructure to outcomes is reshaping what customers expect from a services partner, and it has reshaped how we deliver.

Today, we’re formalizing that evolution: Databricks is launching our Forward Deployed Engineering (FDE) organization. FDE isn't a new practice - it's how some of the most ambitious work at Databricks has been delivered for years. This focus accelerates our current capabilities and brings our Professional Services organization together under one roof with a single mission: accelerate customer business outcomes with AI.

In the last 12 months, our teams have worked with over 1,900 customers to achieve their data and AI goals, from AI-accelerated migrations to building first of its kind AI applications in partnership with Databricks R&D. With Fox, that meant embedding engineers alongside their team to accelerate production-grade innovation across their lines of business:

Building production AI for Fox Corporation means building for passionate fans, around live moments, at massive scale. Our partnership with Databricks FDE brought engineers, not just a platform. Working side-by-side with our FOX engineering teams, Databricks helped us redesign the fan experience across FOX Sports and FOX One using Lakebase, AI Search, Databricks Apps, and Model Serving. The impact has been measurable, with users who engage with Sports AI spending approximately 2X more time in the app, demonstrating how AI-driven experiences can deepen fan engagement at scale. This collaboration accelerated the pace of innovation, bringing AI features to market to drive real time, real world impact. — Melody Hildebrandt, Chief Technology Officer at FOX

We’ve watched the work evolve in real time, and we’ve evolved with it: we first wrote about our OKR-centric delivery model in July 2024, and then shared learnings from our agile service delivery approach. Embedding within accounts and rapidly delivering innovation is core to FDE at Databricks, which has shaped how we partner with customers like JPMC:

We didn't need more consultants, we needed engineers who could build what didn't exist yet. The Databricks FDE team migrated five-plus petabytes of CCB Risk data and 500+ notebooks in four months, trained 600+ of our users on the platform, and accelerated our broader migration beyond the original scope. They cleared the runway for our AI strategy, and we're continuing to partner to build on this foundation. — Bala Vadhiyar, Chief Technology Officer (CTO) | Consumer & Community Banking at JPMC

For many customers, modernization is the path to AI, not a detour from it. Consolidating onto the Lakehouse, retiring legacy warehouses, and rationalizing data pipelines are the first steps that make every downstream outcome possible. FDE engages across that full arc, from the migration that unlocks the platform to the AI applications it powers.

Here’s what makes the Databricks FDE experience different, and what you can expect from working with our team that you can’t get anywhere else.

What Makes the FDE Model Work

Four capabilities converge to make FDE deliver. Together, they are what differentiates Databricks FDE.

Technology: a robust data and AI platform, built for the outcomes customers are asking for

The Lakehouse is the open, unified foundation; Databricks Apps, Genie, and Lakebase extend it into production AI applications, natural-language data access, and operational data. The platform is multi-model and multi-cloud by design. That foundation is the difference between a prototype that demos well and a system that runs your business.

Experience: an engineering-led delivery model

With expertise in data engineering, application development, and deployment of production systems, our FDEs bring the elite engineering talent required to translate the platform's raw potential into a deployed business solution. Our engagements are engineering-driven, centered around customer business objectives.

Scale: a global partner network across regions and skills

Partners are essential to delivering the breadth of expertise FDE demands. Delivering business outcomes requires meeting customers where they are: the technologies they leverage, the industries they operate in, and their location. For years we have worked side-by-side with partners to deliver on our most complex and exciting engagements. We now have hundreds of partners that enable us to bring breadth of expertise and regional coverage to our customers across the globe.

R&D interlock: an extension of our product and engineering teams to customers

FDEs work alongside R&D, with feedback loops that drive rapid iteration. When the platform doesn't yet do what a customer needs, FDE works directly with R&D to extend it. What we learn in the field shapes the product. Joint papers with the research team capture what we learn ( example ). The product expertise that built the platform shows up in the room when you’re solving your hardest problem.

What You Can Expect From Us

Working with FDE is different by design. Here’s what you can expect:

• Measurable outcomes: Engagements anchored on your business outcomes through shared OKRs.

• Reduced time to value: Rapidly move from prototype to production, with fewer handoffs and less context lost. Progress is measured in weeks, not quarters: real, trackable outcomes throughout the engagement.

• Embedded engineering: Engineering talent held to an elite bar, deployed alongside your team.

• Simplified commercial structure: Increasingly outcome-aligned pricing, with milestone-based, fixed-fee options where they fit.

• Scale: Access to a global partner network that scales delivery and brings specialized skills.

What’s Next

If you want to see FDE in action, join us at DAIS on June 15 at the “Build with FDE Hub” section. To get in touch, reach out to your account team at Databricks.

We’re hiring: if you want to build production AI systems with the customers at the cutting edge of technology across every industry, join us .

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