• Architect and implement the unified GTM data spine across CRM, marketing automation, product telemetry, billing, enrichment providers, partner data, and the warehouse. • Establish identity resolution, deduplication, and source-of-truth logic across systems. • Define canonical schemas that we’ll use to operate the business, enable AI across teams and drive results. • Build scalable ELT/ETL pipelines and orchestration workflows tailored to revenue activation. • Implement governance, lineage, access control, and quality monitoring for GTM data assets. • Partner closely with the BI / enterprise data team to align on shared infrastructure while owning GTM-specific models and activation layers. • Develop and productionize segmentation and scoring models (ICP scoring, ABM prioritization, expansion propensity, trial health, pipeline likelihood). • Apply statistical and machine learning techniques to improve scoring, targeting, routing, and revenue predictability. • Design experimentation and measurement frameworks for GTM programs. • Operationalize predictive outputs directly into GTM systems via agents, reverse ETL, or custom integration (Salesforce workflows, outbound automation, lifecycle programs, ABM platforms, partner programs). • Architect customer and prospect intelligence systems integrating first-party, enrichment, and ecosystem signals. • Develop frameworks for ecosystem intelligence (partner influence, marketplace signals, derived demand). • Enable real-time or near-real-time signal activation to sales and marketing teams. • Design structured data systems that power future AI-driven revenue workflows. • Manage and direct external agency/consulting partners executing against the GTM data roadmap. • Establish architectural standards and technical review processes. • Define the build vs. buy strategy for GTM data systems. • Develop the long-term roadmap for in-house data capability.