Director, Revenue Analytics & AI Innovation

Our mission: eliminating every barrier to mental health.

Spring Health is a global mental health company on a mission to eliminate every barrier to mental health. We're building a world where getting support is simple, personal, and built around the person, so care can continue through every job, move, health plan, and life stage. Our AI-native platform helps us deliver personalized support across self‑guided tools, coaching, therapy, medication management, and specialty care.

With outcomes independently validated by JAMA Network Open and the Validation Institute, Spring Health reaches more than 170 million people worldwide through leading employers, health plans, and partners. As an AI-native company, we believe technology should expand the reach, quality, and humanity of care. Every Spring Health team member is expected to use AI tools thoughtfully, apply human judgment to AI outputs, and keep building AI fluency in ways that support their role and our mission.

Reporting to the VP, Revenue Operations and partnering closely with our Systems and Strategy & Operations teams, the Director, Revenue Analytics & AI Transformation will serve as the semantic layer of our AI‑native Rev Ops organization. This role will be responsible for building the unified data models and AI transformation strategy that power decisions from individual contributors to the executive team.

This is a full‑time, fully remote role leading a small, highly technical team as a hands‑on player‑coach. NYC is preferred for this role, but we are open to considering remote candidates. Travel is limited, with occasional trips (approximately quarterly) for team and company off‑sites.

• Architect and own the unified semantic and data layer that underpins Revenue Operations — the single source of truth connecting Sales, Marketing, Customer Success, and Finance data.
• Define and drive the AI transformation strategy for Revenue Operations, identifying where AI and automation can meaningfully improve GTM efficiency, forecasting, and decision‑making.
• Build and lead a small, highly technical team of data/analytics engineers, operating as a true player‑coach who still writes code and ships models personally.
• Design and maintain core data models, pipelines, and analytics products that deliver actionable insight across the revenue organization — from individual contributors to executives.
• Partner closely with the Systems and Strategy & Operations teams to ensure data architecture, tooling, and process are tightly aligned across Rev Ops.
• Evaluate, pilot, and deploy AI tools and vendors, staying ahead of the fast‑moving AI landscape and translating emerging capabilities into practical, high‑value use cases.
• Establish data governance, quality standards, and documentation practices that scale as the organization grows.
• Build executive‑ready dashboards and analyses that inform revenue strategy and company‑wide reporting.
• Champion a "build fast, iterate faster" culture — rapidly prototyping and deploying solutions rather than waiting for a long, perfect build cycle.
• Serve as a thought partner to Revenue leadership on how AI and modern data infrastructure can reshape the future of go‑to‑market operations.

• Reduction in time‑to‑insight for revenue questions, measured by cycle time from request to delivered analysis.
• Adoption of unified data models and dashboards across Sales, Marketing, and Customer Success teams.
• Number of AI‑driven use cases successfully piloted and deployed into production each quarter.
• Improved forecast accuracy and reporting consistency across the revenue funnel.
• Percentage of core revenue reporting migrated off manual/spreadsheet‑based processes onto the unified data layer.

• 10+ years of experience in data engineering, analytics engineering, or revenue/GTM analytics, including experience leading or building technical teams.
• Deep hands‑on expertise in data engineering and analytics engineering (e.g., SQL, dbt, modern data warehousing, ETL/ELT pipelines).
• Proven experience designing and owning semantic and data models that serve multiple stakeholders, from analytics and BI to AI use cases.
• Demon…