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
Pay Range Transparency
Databricks is committed to fair and equitable compensation practices. The pay range(s) for this role is listed below and represents the expected base salary range for non-commissionable roles or on-target earnings for commissionable roles. Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to job-related skills, depth of experience, relevant certifications and training, and specific work location. Based on the factors above, Databricks anticipated utilizing the full width of the range. The total compensation package for this position may also include eligibility for annual performance bonus, equity, and the benefits listed above. For more information regarding which range your location is in visit our page here.
Zone 1 Pay Range
$217,800—$299,400 USD
Zone 2 Pay Range
$196,000—$269,500 USD
Zone 3 Pay Range
$185,100—$254,550 USD
Zone 4 Pay Range
$174,200—$239,600 USD
About Databricks
Databricks is the data and AI company. 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. To learn more, follow Databricks on Twitter, LinkedIn and Facebook.
**Benefits
**At Databricks, we strive to provide comprehensive benefits and perks that meet the needs of all of our employees. For specific details on the benefits offered in your region click here.
Our Commitment to Diversity and Inclusion
At Databricks, we are committed to fostering a diverse and inclusive culture where everyone can excel. We take great care to ensure that our hiring practices are inclusive and meet equal employment opportunity standards. Individuals looking for employment at Databricks are considered without regard to age, color, disability, ethnicity, family or marital status, gender identity or expression, language, national origin, physical and mental ability, political affiliation, race, religion, sexual orientation, socio-economic status, veteran status, and other protected characteristics.
Compliance
If access to export-controlled technology or source code is required for performance of job duties, it is within Employer's discretion whether to apply for a U.S. government license for such positions, and Employer may decline to proceed with an applicant on this basis alone.