Lead GTM Enablement & Scale Architect, Lakebase

SLSQ227R703

Lead GTM Enablement & Scale Architect - Lakebase, Field Engineering

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

Lakebase is Databricks' managed, serverless PostgreSQL database built for AI applications and agents - a category-defining product that bridges transactional, analytical, and AI workloads on a single governed platform. 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 Lakebase from early adoption to a product every Solutions Architect in the field can confidently qualify, position, demo, and defend in competitive situations. You will be the connective tissue between the Lakebase 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.

What You'll Do

• Own the global GTM and enablement strategy for Lakebase 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 the Lakebase 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 Lakebase deals


• 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. 

What We Look For

• 8+ years in solutions architecture, technical pre-sales, developer relations, technical product marketing, or technical enablement, with direct experience in databases, 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 PostgreSQL, OLTP databases, or cloud database services


• 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 distributed systems concepts accessible to a broad technical audience


• Familiarity with the data and AI ecosystem: Lakehouse architecture, Delta Lake, vector databases, AI/ML serving patterns

Nice to Have

• 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 operational databases (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