GTM Data and Systems Lead

What you'll be doing:
We're looking for a GTM Data and Systems Lead to build the data foundation that our go-to-market organization runs on. As Basis grows across products and sales motions, you'll define how we represent customers, measure performance, and give agents the context to work effectively.

You'll own the GTM data model and the systems that keep it accurate. That means designing the architecture, writing code, administering Salesforce, and fixing integrations when they break. RevOps owns the operating processes; you'll translate agreed business requirements into systems.

You'll report to our Head of GTM AI Ops and work closely with Revenue Operations, Sales, Marketing, and Customer Success. You'll start as a hands-on senior individual contributor, with the opportunity to build and lead the GTM Data and Systems function over time.

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.

Location: NYC, Flatiron office. In person, five days per week.

What you'll bring:
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.

What we'd love to see:
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.

What success looks like in this role:
Trusted answers: Teams use shared definitions and can explain the numbers without rebuilding them in separate spreadsheets.

Dependable systems: Broken syncs and data drift are detected, investigated, and reconciled against their sources.

Useful agents: Agents complete approved work with traceable actions and clear escalation when context or permission is missing.

Room to grow: New products and GTM motions fit the model without repeated rebuilds.