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SLSQ227R703
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.
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.
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.
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
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
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
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.
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.
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.
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.
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Databricks provides a unified cloud platform for data analytics and artificial intelligence, enabling enterprises to manage data pipelines, data warehousing, and machine learning workloads. The company serves data engineers, data scientists, and analysts across various industries who need to build and deploy AI applications at scale.
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