Lead Product Manager / Solutions Architect - CPQ Commerce Applications

Commerce Application Product Management Role

The Commerce Application Product Management group owns the systems that power how Docusign transacts, grows, and retains its customer base spanning quoting, billing, provisioning, and entitlements through account lifecycle at enterprise scale. You will own commerce capabilities end to end connecting how Docusign's products are defined, priced, quoted, billed, and provisioned into a coherent, reliable system that scales with the business. This role sits across the full commerce stack, requiring someone who can hold the whole arc in mind: from the data models and governance that underpin product and pricing constructs, through the quoting and ordering controls that govern how customers and sellers transact, to the downstream fulfillment and revenue systems where commitments become outcomes. This is a solution architect's role as much as a product manager's. You will be expected to map how decisions in one layer ripple into others, identify where fragmentation creates systemic risk, and drive the integrations and standards that make the commerce stack coherent rather than merely functional. Domain depth in any part of the stack is valuable; the ability to reason across all of it is essential.

This position is an individual contributor role reporting to the Director, Applications Product Management - Commerce.

Responsibilities

• Own the product vision, strategy, and roadmap for commerce capabilities end to end spanning quoting controls, product and pricing enablement, and the integrations that connect these to billing, provisioning, and revenue operations across direct, partner, and self-serve channels
• Map and manage the full commerce stack: understand how data, decisions, and system states flow from product definition through quoting, order capture, billing, and fulfillment and use that understanding to identify gaps, reduce fragmentation, and drive systemic improvements
• Define and enforce quoting controls: pricing rules, discount guardrails, configuration validation, approval policies, and contract structuring logic that ensure every quote is compliant, accurate, and ready to fulfill
• Drive the end-to-end quoting experience for sellers and customers reducing friction, improving accuracy, and shortening time-to-quote by addressing root causes of rework and errors across system and process boundaries
• Identify systemic issues that degrade commerce performance and drive corrective action that prevents recurrence, partnering cross-functionally to resolve complex issues end to end
• Enable new monetization models and pricing constructs to reach market accurately and on time defining the data standards, workflows, and governance (including NPI and catalog management) that make product and pricing launches reliable and repeatable rather than fragile and ad hoc
• Partner with Engineering, Product and Technology, Finance, Sales Operations, and GTM teams to align commerce capabilities with business policies, pricing strategy, and go-to-market motions connecting team work to business outcomes
• Define and drive the AI-first commerce experience strategy - designing quoting and ordering flows where AI is embedded natively, not layered on top, to deliver outcomes that are not achievable through conventional product design alone
• Own the product roadmap for AI-powered commerce capabilities: intelligent configuration assistants that guide sellers and customers to valid, optimized quotes; real-time pricing recommendations grounded in deal context and business rules; natural language interfaces that reduce reliance on form-heavy workflows; and agentic automations that resolve errors, route approvals, and surface anomalies without human escalation
• Define how AI capabilities are evaluated, measured, and improved over time - establishing the feedback loops, accuracy benchmarks, and confidence thresholds that determine when AI recommendations are trusted, surfaced, or escalated to human review
• Partner with engineering and data science teams to shape the AI development lifecycle for commerce features: from problem framing and data requirements through prompt and model evaluation to staged rollout and post-launch learning
• Establish observability across commerce platforms: dashboards, accuracy SLAs, AI performance metrics, readiness scorecards, and operational health signals that make platform and model performance visible and actionable for leadership
• Mentor and develop talent across the team, contributing actively to hiring and cross-functional team growth; lead without authority and drive alignment across diverse stakeholders
• Use AI tools (e.g., Claude, Gemini, Glean, Copilot) to prototype, document, and continuously improve team workflows and delivery practices