Lead GTM Enablement & Scale Architect, Field Engineering - Customer Skills

FEQ327R617

Lead GTM Enablement & Scale Architect, Field Engineering - Customer Skills

About Databricks

More than 10,000 organizations worldwide - including Comcast, Condé Nast, Grammarly, and over 50% of the Fortune 500 rely on the Databricks Data Intelligence Platform to unify and democratize data, analytics and AI. Databricks is headquartered in San Francisco, with offices around the globe and was founded by the original creators of Lakehouse, Apache Spark™, Delta Lake and MLflow.

The Impact You Will Have

Pure technical knowledge used to be the main factor in winning customer trust. But in a world where AI can generate prototypes and answer complex queries instantly, being a walking feature catalog isn't enough anymore. What truly sets a great Technical field apart today is their ability to connect with customer leadership, understand real business problems, and build meaningful trust. Technical skills get us into the room, but strong judgment and conversation leadership win the partnership.

In this role, you will shape that evolution for a global team of over 3,000 Field Engineers (Solutions Architects). Rather than following an existing playbook, you’ll help write the strategy to elevate our engineers from skilled technical builders into trusted business advisors. This isn't about generic soft-skills workshops—it's about teaching customer influence within the real-world context of the Databricks platform, our deals, and executive decision-making. You will define what true trusted advisorship looks like here and build practical programs that translate directly into real customer conversations.

What You'll Do

• Own the global strategy for Customer Skills across Field Engineering - the frameworks, the standard, and the definition of what separates a technical specialist from a trusted advisor - and then execute it end to end
• Build the influence approaches in the context of the platform: discovery, working backwards from business outcomes, executive engagement, value framing, and consumption-driven selling, all taught against real Databricks deals and real customer scenarios, never in the abstract
• Elevate the field from builder to builder-plus-advisor: define the behaviors that matter now that AI has lowered the technical barrier, and build the programs that develop judgment, presence, and the ability to influence a CxO
• Build and ship enablement at scale using AI: use Claude Code, vibe coding, and AI content pipelines to generate first-draft practice scenarios, role-play prompts, coaching content, and personalized learning journeys, then curate for impact
• Design and build AI-powered practice environments - executive role-plays, scenario simulators, personalized coaching agents - that SAs use in their flow of work, so practice happens before the customer meeting, not after the deal is lost
• Partner directly with FE leadership, the People Team, and enablement peers to embed customer skills into the hiring bar, performance management, and career development, so this becomes how we hire, coach, and promote, not a one-time program
• Enable SAs and account teams to break out of the IT silo and drive consumption at the CxO level
• Establish a tight leadership feedback loop: systematically surface where the field stalls - in discovery, in the executive conversation, in converting a technical win into consumption - and bring those insights back to FE leaders with recommendations they can act on. 
• Create enablement that changes behavior, not completion rates: workshops, AI simulations, peer coaching frameworks, and certification paths, always biased toward what shows up in the next customer conversation
• Stay a practitioner of what you teach: spend ~10-15% of your time leading the customer executive conversations you train SAs on - EBCs, CxO briefings, strategic account reviews. You cannot build trusted-advisor enablement from outside the room
• Define and track KPIs that measure behavior change: SA confidence, quality of customer engagement, deal progression velocity, and consumption growth in enabled accounts

What We Look For

• A technical leader who has built customer-facing skill at scale inside a large, global technical organization - you have done a version of this job before, for hundreds or thousands of people, not twenty
• You have been the SA in the room, or in a role close enough that you have run discovery, handled an executive on the fly, and turned a technical win into consumption. That lived experience is what makes your enablement credible to a skeptical field
• A strong point of view on what AI changes for technical talent: you understand that when execution gets cheap, influence and judgment become the scarce skills, and you can articulate what that means for how we develop the field
• Deep understanding of how technical people learn and what actually shifts their behavior, not just how to transfer knowledge
• Builder mentality: you default to building tools, AI workflows, and practice systems, not decks. You use AI tools as a daily force multiplier
• Demonstrated ability to build programs from scratch (0-to-1). You see a blank page as an opportunity, not a problem
• Experience working with senior leaders as a peer, with the backbone to challenge them when the data says the current approach isn't working
• Scaling mindset: everything you build has to work for a 3,000-person technical field across AMER, EMEA, and APJ. You think about leverage and automation before live delivery
• Exceptional communication and facilitation: you can run a room of senior SAs and make it worth their time, and write for an asynchronous global audience just as well
• Fluency in executive engagement and strategic selling methodologies (Challenger, MEDDPICC, Value Selling, or similar), enough to teach them, not just name them

Nice to Have

• Experience at a high-growth infrastructure or SaaS company through a scaling inflection point
• A technical background you can still draw on - you have enough platform depth to earn credibility with SAs and to teach influence in the context of real architecture and real deals
• Background across both pre-sales and post-sales - you have lived the full customer lifecycle
• Hands-on experience with Databricks or other data and AI platforms
• Experience running executive engagement or C-level advisory programs
• You have already used AI to build at scale - automating content creation, building internal tools, or shipping learning experiences faster than anyone expected