Sr Product Manager, Opportunity-to-order

Cribl · Enterprise · CA · IT & Security

This role focuses on defining and improving the opportunity-to-order (O2O) systems and workflows within a B2B SaaS company, with a significant emphasis on integrating AI and automation to enhance predictability, speed, and confidence in the revenue lifecycle. The Sr. Product Manager will own the roadmap for systems governing deal structuring, quoting, approvals, contracting, and renewals, ensuring data quality and system integration to support AI use cases and reliable reporting.

What you'd actually do

  1. Establish a clear product vision for how Salesforce, DealHub, Ironclad, Clari, and adjacent tools, including AI, work together to support deal design, quoting, approvals, contract workflows, and renewals.
  2. Guide how DealHub and related CPQ patterns evolve so that complex deal structures, ramps, and subscription changes remain simple for users to configure, price, and approve as Cribl's product mix and commercial models expand, while keeping the experience ready for AI-assisted selling and approvals.
  3. Strengthen the underlying data models, validation patterns, and integrations across systems so that ARR reporting, forecasting, and AI use cases rest on reliable, well-defined revenue data rather than fragile one-off logic.
  4. Influence how Ironclad and Salesforce work together so that contracts, questionnaires, and security artifacts are easy to generate, review, and connect back to the quotes and opportunities they support, structured in ways that unlock AI-assisted review and insight.
  5. Create space for well-designed AI experiments with clear hypotheses, guardrails, and success measures, so the business can tell which AI investments are actually improving revenue outcomes and where to scale or pivot.

Skills

Required

  • 5+ years of progressive product management in B2B SaaS
  • Direct ownership of revenue, CPQ, or opportunity-to-order systems
  • Hands-on experience with modern CPQ (DealHub, Salesforce CPQ, or similar) in a Salesforce Sales Cloud environment
  • Strong understanding of how quoting, approvals, subscription management, and renewals connect across the revenue lifecycle
  • Demonstrated history of defining and shipping AI use cases in internal business workflows
  • Strong understanding of opportunity stages, quoting and approvals, discounting patterns, renewals, and ARR or revenue modeling in a modern SaaS business
  • Comfort reasoning about how data flows through integrated systems
  • Proven ability to build strong relationships across a wide set of stakeholders
  • Experience using data and metrics to assess product impact
  • Able to work effectively with engineering systems teams, translating business requirements into product direction, surfacing implementation tradeoffs, and writing clear acceptance criteria
  • Exceptional written and verbal communication

What the JD emphasized

  • AI use cases in internal business workflows, not only prototypes
  • AI experiments with clear hypotheses, guardrails, and success measures

Other signals

  • AI strategy for revenue systems
  • AI use cases in internal business workflows
  • AI experiments with clear hypotheses and success measures