GTM Data and Systems Lead

Basis AI•New York, NY

10d•$150,000 - $250,000•Onsite

About The Position

Basis is seeking a GTM Data and Systems Lead to establish the data infrastructure for its go-to-market organization. This role involves defining how customer data is represented, measuring performance, and providing context for agents. The lead will own the GTM data model and associated systems, including designing the architecture, coding, administering Salesforce, and managing integrations. The position translates business requirements into system configurations and works closely with Revenue Operations, Sales, Marketing, and Customer Success. Initially a hands-on senior individual contributor role, there is potential to build and lead the GTM Data and Systems function. Key responsibilities include building the GTM data model to define customer lifecycle entities, ensuring consistent metrics through a shared semantic layer, providing reliable context for agents via a GTM ontology, managing Salesforce and connected systems, maintaining integrations with tools like Outreach, Gong, and Clay, capturing customer activity events, building agents for system automation and improvement, and owning the technical roadmap for future growth.

Requirements

Experience owning GTM systems or data architecture at a growing B2B company, with systems you've built and maintained in production.
Deep Salesforce administration skills, including relational modeling, flows, validation rules, permissions, and change management.
Strong SQL and hands-on coding in Python or TypeScript/JavaScript. You can build and debug integrations beyond native connectors.
Experience translating business questions into data models, metric definitions, and reporting that stays consistent across tools.
Sound engineering judgment about testing, monitoring, access controls, and safe migrations. You can explain a trade-off and carry the work through to adoption.

Nice To Haves

Semantic layers, analytics engineering, or business ontologies used by both humans and AI systems.
Agents or LLM workflows operating against business systems, with evaluations and controlled writes.
Event-driven architecture, identity resolution, or reliable integrations across a complex GTM stack.
Experience growing a technical function, mentoring builders, or supporting multiple products and revenue models.

Responsibilities

Build the GTM data model: Define accounts, people, opportunities, products, and their relationships across the customer lifecycle. Support multiple buying groups, sales motions, and expansion paths without losing history.
Make metrics consistent: Build the semantic layer: shared definitions and calculation logic for pipeline, conversion, revenue, and customer health. Partner with RevOps and Finance on definitions, then deliver reporting people can trace to source records.
Give agents reliable context: Define the GTM ontology: business entities, relationships, and rules that explain what the data means. Make that context available through documented, permission-aware interfaces that people and agents can use.
Own Salesforce and connected systems: Manage objects, fields, flows, permissions, and data quality. Own configuration, troubleshooting, user onboarding, and tested releases across the GTM stack.
Keep integrations reliable: Build and maintain APIs, webhooks, and bidirectional syncs across Salesforce, Outreach, Gong, Clay, and other GTM tools. Define which system controls each field. Handle retries, duplicate events, conflicts, and recovery without overwriting valid data.
Capture events and signals: Build sensors for customer activity and buying signals. Preserve identity, source, timing, and outcomes so teams can distinguish attempted activity from engagement and measure what moves accounts forward.
Build agents that improve the systems: Automate data-quality checks, sync monitoring, and investigation of broken workflows. Build agents that propose and implement tested improvements within approved permissions, with clear escalation for changes that need human judgment.
Build for the next stage: Own the technical roadmap with AI Ops and RevOps. Make build-versus-buy decisions, document the system, and develop the practices that a growing function can build on.