GTM Engineer

This is a high-ownership, builder-first role for someone who wants to work at the intersection of GTM strategy and hands-on systems engineering: architecting the data model, shipping the automation, and building the AI agents that let Grow's commercial teams do more without growing headcount linearly.

A taste of the systems you could be building

• Primary Care (PCP): Design the data model and automation that convert physician-office outreach into a repeatable, inbound referral engine, and automate referral intake, validation, and routing.
• Employee Assistance Program (EAP): Map the end-to-end consultant → employer → employee journey and build the staging environment and data model from scratch to support a brand-new motion.
• Provider Experience: Build the recruitment, credentialing, and retention pipeline that moves a provider from first application to an active, engaged member of Grow's network.

What You'll Be Doing

• Architect the system: Design and ship the end-to-end technical architecture for your assigned motion, spanning data model, automation logic, and integration patterns.
• Build AI agents, not just workflows: Design and deploy AI agents (built on Claude) that autonomously perform GTM tasks (referral triage, document parsing, account research) rather than workflows that simply call an LLM API.
• Run an experimentation loop: Treat new automations and agent behaviors as experiments: ship fast, measure impact, and iterate or kill based on data, not intuition.
• Evolve the stack: Continuously evaluate the GTM tech stack (HubSpot, n8n/Workato, Segment, Gong, Aircall/Nooks, Clay) and recommend consolidation or upgrades as your motion matures and scales.
• Sandbox everything: Implement rigorous testing and deployment so no logic goes live unvalidated.
• Document the system: Maintain a technical schema map of your systems so the organization understands the logic behind every automation.
• Own data governance: Define and enforce data governance standards (field definitions, ownership, quality rules, and access controls) for your data model as a core part of data strategy.
• Build for resilience: Build monitoring and self-healing automations that alert the team when data syncs fail or logic breaks.
• Align on shared architecture: Partner with the GTM Engineering Lead, Enterprise & GTM Architecture to apply consistent technical standards and governance practices across your systems and the rest of Grow's GTM architecture.

Outcomes You'll Own

Across PCP, EAP, and Provider Experience, the bar is the same: build systems that let volume grow without a proportional increase in manual effort.

• Grow the demand side (Primary Care): Increase the number and quality of inbound physician referrals and successful patient intakes.
• Build the EAP foundation (EAP): Ship a 0-to-1 systems foundation robust enough to support Grow's next phase of employer and consultant growth.
• Maximize efficient supply (Provider Experience): Increase the number of credentialed, engaged therapists and prescribers Grow can support without a proportional increase in manual effort.

You'll Be a Good Fit If You Have Experience

• 5+ years in a technical RevOps or GTM Engineering role; experience with healthcare, benefits, credentialing, or referral/recruiting systems is a plus.
• An Architect Mindset: You see logic gates, data schemas, and API endpoints, and design for scale rather than one-off fixes.
• HubSpot Mastery: Expert-level technical knowledge of HubSpot (Advanced Workflows, Custom Objects) as Grow's core CRM.
• Orchestration Fluency: Expert proficiency in n8n and Workato, and comfort integrating tools like Segment, Gong, Aircall, Nooks, and Clay, depending on your track.
• AI Agent Builder: Proven experience building AI agents and agentic workflows (Claude, OpenAI, or Gemini) that autonomously perform GTM tasks, not just single-step LLM calls.
• 0-to-1 & Multi-Stakeholder Comfort: Experience standing up net-new systems and data models for a channel or motion that didn't previously exist, and/or designing CRM data models for complex, multi-party relationships.
• Data Governance: Experience setting data governance standards (definitions, ownership, quality rules, access controls) as a core part of data strategy, not an afterthought bolted on once a model is live.
• Lifecycle Thinking: Experience architecting systems that span both a funnel and an ongoing relationship, not just a single-stage pipeline.