Director, Revenue Analytics & AI Innovation

- 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 RevOps.

- 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.

- Demonstrated experience leading AI adoption or AI-enabled transformation within a GTM or Revenue organization.

- Strong, current knowledge of the AI vendor, tooling, and trends landscape, with the judgment to separate hype from real value.

- A track record as a "player-coach" - someone who has built and led a small team while remaining deeply hands-on.

- Experience partnering closely with Revenue/GTM systems and strategy & operations functions.

- Excellent communication skills, with the ability to translate technical work into business impact for individual contributors and executives alike.

- A scrappy, fast-moving builder mentality - comfortable shipping v1 solutions quickly and iterating.

- Experience in a high-growth SaaS or healthtech environment.

- Familiarity with modern AI/ML tooling (LLM-based agents, copilots, automation platforms) applied to go-to-market use cases.

- Hands-on experience with tools such as Salesforce, Clay, Snowflake or BigQuery, dbt, Looker or Tableau, and Python

- Prior experience at a company undergoing significant data infrastructure modernization.

The target base salary range for this position is $196,000 - $247,940 and is part of a competitive total rewards package including equity and benefits. Individual pay may vary from the target range and is determined by a number of factors including experience, location, internal pay equity, and other relevant business considerations.

We review all employee pay and compensation programs annually using Radford Global Compensation Database at minimum to ensure competitive and fair pay.