Lead GTM Enablement & Scale Architect, Product Enablement

SLSQ227R703

Databricks ships category-defining products across its portfolio - from Data Warehousing and Real-Time Analytics, to Data Engineering, AI and Agents, Business Intelligence and Genie, and Operational Data. This is a founding enablement role. You won't be inheriting a playbook - you'll be writing it. You will own the end-to-end enablement strategy that takes a Databricks product area from early adoption to something every Solutions Architect in the field can confidently qualify, position, demo, and defend in competitive situations. You will be the connective tissue between the Product team and a global field of SAs and Partner Technical Sales - translating product capabilities into customer outcomes, and representing the field-readiness voice in Product forums where the story is unclear, where SAs stumble on positioning, where the demo surface needs to tighten before it reaches the field.

• Own the global GTM and enablement strategy for your Databricks product area for Field Engineering and Partner Technical Sales - from foundational knowledge through advanced competitive positioning
• Build and ship enablement at scale using AI: use vibe coding, and AI content pipelines to generate first-draft technical deep dives, competitive talk tracks, hands-on labs, and demo environments - then curate for accuracy and field impact
• Drive a 'builder-first' SA culture by architecting scalable demo environments and POC repositories designed for forking, rapid customization, and deep technical proof-of-concept delivery.
• Partner directly with Product and Engineering leadership to stay ahead of the roadmap and translate upcoming features into field-ready assets before GA
• Establish a tight product feedback loop - systematically capture field friction, lost deals, and SA objections and channel them back to Product with actionable recommendations. You have the standing to tell PMs what's not working and the data to back it up
• Design the competitive narrative architecture and build the "why Databricks" story that gives an SA confidence walking into a room with a customer executive.
• Create scalable, multi-format enablement: Deep dives, solutions, AI role-plays, hands-on labs, and self-paced learning paths - always with a bias toward assets SAs can use in a customer conversation immediately
• Build AI-powered tools that make the field smarter: agents for instant answers, AI role-plays for pitch practice, automated competitive briefs from real-time market signals
• Define and track KPIs that measure field readiness, and whether SAs are actually winning more deals in your product area
• Stay a practitioner yourself: spend ~10-15% of your time in customer-facing moments - customer executive briefings, select competitive POCs, because what you build is sharper when you've defended the position in front of a customer executive, not just written it down
• Take a step back, think strategically and innovate your approaches to keep up with the fast paced environment.

• 8+ years in solutions architecture, technical pre-sales, developer relations, technical product marketing, or technical enablement, with direct experience in data and AI platforms, distributed systems, or cloud data infrastructure
• You've been the SA in the room: you know what it feels like to run a POC, handle objections live, and defend a technical position against a competitor. That lived experience is what makes your enablement credible
• Deep hands-on knowledge of modern data and AI platforms, and depth in one or more Databricks product domains (Data Warehousing, Data Engineering, AI/ML, BI, or operational databases)
• Builder mentality: you default to building tools, demos, and automations, not decks. You use AI tools as a daily force multiplier, not a novelty
• Demonstrated ability to build enablement programs from scratch (0-to-1), not just iterate on existing content. You see a blank page as an opportunity, not a problem
• Strong product instinct: you can look at a feature roadmap and immediately see how it maps to customer use cases and competitive differentiation
• Experience working directly with Product and Engineering teams as a peer, not just a consumer of their content
• The backbone to tell Product "the field can't sell this because X" - backed by data and field evidence
• Scaling mindset: everything you build needs to work for a global field team, not a 20-person workshop. You think about leverage and automation before you think about live delivery
• Exceptional communication skills - you can make complex technical concepts accessible to a broad technical audience
• Familiarity with the data and AI ecosystem: Lakehouse architecture, Delta Lake, vector databases, AI/ML serving patterns

• Experience at a high-growth infrastructure company during a major product launch
• Background in both pre-sales and post-sales technical roles - you've lived the full customer lifecycle
• Hands-on experience with Databricks or competitive platforms
• Experience building AI applications on modern data and AI platforms (RAG patterns, agent architectures, etc.)
• You've already used AI to build at scale - automating content creation, building internal tools, or shipping demos faster than anyone thought possible