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The Lead GTM Enablement & Scale Architect will develop and implement enablement strategies for Lakebase, ensuring field readiness and competitive positioning while leveraging AI for scalable solutions.

Lead day-to-day operation and maturation of Databricksprivacy compliance program, develop AI-enabled controls, run privacy by design and risk assessments, manage data subject rights, contracts (DPAs, SCCs), regulatory engagement, incident response, policy development, cross-functional training, and continuous process improvement across the company.

The Senior Customer Enablement Architect drives customer success through tailored training programs, partnering with sales teams and assessing skill gaps to enhance adoption and business outcomes for Databricks' products.

Lead compensation analytics and AI strategy to transform compensation data into insights, tooling, and forecasts. Own market benchmarking, total comp budget forecasting, competitive intelligence, and partner with Finance and senior leaders on compensation design and equity modeling while ensuring data governance and privacy.

Lead GTM compensation strategy and programs: design pay frameworks, manage lifecycle compensation processes, analyze market and comp data, advise founders and senior leaders, and scale enterprise-wide compensation solutions.

Run and own AI-first sales development programs: design metrics, reporting, governance, adoption, and sequence management. Drive executive cadences, prioritize with sales leaders, build data foundations and field-facing reporting, and partner across AI Ops, Sales Ops, GTM Analytics, and Enablement to scale AI-native prospecting.

Lead and scale Databricks' global web marketing function: build a product-focused team, modernize web properties for humans and AI agents, implement personalization and testing, apply AI to automate web workflows, ensure machine-readable content and strong information architecture, drive analytics-informed growth, manage vendors, and coordinate global marketing efforts.

Design and maintain a skill and concept graph, build ML models to infer learner skills, and develop recommendation systems and LLM-driven features. Ship production-ready LLM systems, define explainability standards, partner with frontend and product teams, and monitor model performance and evaluation frameworks.

Act as the field product manager for high-value AI engagements: partner with Sales to scope C-suite problems, define MVPs and PRDs, prioritize engineering backlogs, run agile delivery, lead launch and adoption, and hand off ongoing operations while proving ROI.

Customer-facing, hands-on engineer who architects and delivers production-grade data and AI solutions on Databricks. Responsibilities include end-to-end design, building data pipelines, ML/AI integration, deployment, stakeholder engagement, reusable assets creation, and guiding customers on architecture and adoption.