Principal Product Marketing Manager

Gong Gong · Enterprise · San Francisco, CA · Product Marketing

Principal Product Marketing Manager for Gong's agentic execution layer, focusing on shaping the narrative and go-to-market strategy for technical buyers evaluating enterprise AI infrastructure for revenue. This role requires deep understanding of agentic workflows, governance, and data foundations to position Gong's integrated AI and execution capabilities.

What you'd actually do

  1. Own the technical buyer narrative — define positioning and messaging that earns credibility with CIOs and RevOps leaders evaluating enterprise AI infrastructure for revenue. Go beyond outcomes: make governance models, orchestration architecture, data permissioning, and agent lifecycle management feel purpose-built and immediately trustworthy.
  2. Build the infrastructure conversation — develop the content, frameworks, and tools that help CIOs and RevOps architects understand how Gong fits into their AI stack. This means data flows, governance controls, agent coordination, MCP integrations, audit trails, and the role of the Gong Revenue Graph as the grounding layer that makes autonomous execution reliable at enterprise scale.
  3. Unify AI and execution into a single story — intelligence and execution are one product. Your job is to make the integrated picture feel inevitable to a technical buyer who has spent the past 18 months evaluating fragmented point solutions and wondering why none of them hold up in production.
  4. Take a portfolio view — make and defend recommendations about how AI execution capabilities are packaged and monetized across the platform.
  5. Shape product and GTM strategy — bring structured buyer, competitive, and analyst insights into product planning; develop a deep POV on how enterprise IT and revenue operations teams evaluate, adopt, and govern agentic AI; be the team’s expert on how this market is evolving and how enterprises are making platform decisions.

Skills

Required

  • 8+ years of B2B product marketing experience
  • at least four years owning the full PMM lifecycle for an AI platform, agentic infrastructure, or complex workflow product at an enterprise SaaS company
  • demonstrated success marketing to technical buyers
  • Working knowledge of agentic workflow concepts: triggers, actions, plays, next-best-action, human-in-the-loop governance, MCP integrations, and how enterprises think about AI governance and data residency.
  • Exceptional written and verbal communication skills
  • distill architectural concepts into narratives that earn trust
  • adapt your message to a variety of audiences across business and technical audiences
  • A bias toward evidence and action — you test positioning with customers, validate it in win/loss data, and iterate based on what actually moves technical buyers.
  • Comfort operating in ambiguity and at pace
  • Experience marketing to RevOps, Sales Operations, or Revenue Leadership (CRO, VP Sales) personas.
  • A track record of making complex, multi-layered systems feel coherent and inevitable to skeptical enterprise buyers.
  • Prior experience working on a product that was actively defining or expanding a market category, not just competing in an established one.
  • Experience with analyst relations (Gartner, Forrester) — you have presented at briefings, shaped Magic Quadrant narratives, or contributed to Wave evaluations.
  • Comfort with quantitative analysis: win/loss data, pipeline influence metrics, adoption curves, and consumption/credit pricing models.

Nice to have

  • AI-assisted coding tools

What the JD emphasized

  • technical buyer audience
  • agentic AI for revenue
  • governed execution
  • agent sprawl
  • coordinated operating model
  • data foundation underneath agents
  • enterprise AI infrastructure
  • governance models
  • orchestration architecture
  • data permissioning
  • agent lifecycle management
  • agentic workflow concepts
  • AI governance
  • data residency
  • multi-layered systems
  • governance models
  • data permissioning
  • orchestration logic
  • agent lifecycle management

Other signals

  • AI-powered intelligence
  • specialized agents
  • agentic execution layer
  • governed execution
  • agent sprawl
  • coordinated operating model
  • data foundation underneath agents
  • agent lifecycle management