Marketing Operations Manager

We're building an agentic AI platform that helps enterprises transform complex business workflows into AI-powered operations. We work with leading global companies to identify high-value workflows, deploy AI agents into real business environments, and help organizations move from experimentation to measurable enterprise impact. We believe AI adoption inside the enterprise isn't just a technology challenge. It requires business context, workflow understanding, change management, trust, ownership, and a clear path to value. That's where this role comes in. We're a well-funded, early-stage startup, hiring a Marketing Operations Manager to own the machinery under our funnel and build it from the ground up. This role is ideal for a hands-on, systems-minded operator who thrives in early-stage environments, is comfortable creating structure where none exists, and works closely with marketing leadership to define how our funnel actually runs. You will own the funnel end to end, from how a lead is created through how we measure what it becomes. You will also build the system itself: the lifecycle, the data, the tooling, and the standard everyone downstream trusts the numbers by. Marketing has grown fast here, and the systems underneath it now need a single owner. You'll decide what qualifies as a lead, prove where pipeline actually comes from, and build the machine that keeps that honest as we scale. Your Impact Lead Lifecycle & Routing Define what counts as a lead, and how leads enter from inbound forms, events, paid channels, outbound, partners, and product signals Design the lifecycle end to end: stage definitions, lead statuses, scoring models, routing rules, and SLA enforcement Own the handoff to SDRs and AEs, so nothing gets dropped between marketing and sales Segmentation & Data Build and own the lists demand gen, field marketing, and the SDR team run against, from named accounts to ICP fit criteria Own enrichment and data hygiene, so the segments people target are actually current When a campaign owner needs a segment, you decide how it's defined and how it stays alive Tools & Tracking Own every marketing tool we run: evaluation, procurement, admin, integrations, seats, and renewals Own the tracking layer, and make sure the codes fire and the data lands where the reporting expects it Measurement & Attribution Design the attribution model and decide what earns credit, and at what point Build reporting that shows funnel conversion by stage, channel performance, cost per qualified meeting, and pipeline contribution Make quarterly numbers come out of a dashboard, with the model's blind spots documented, not discovered late HubSpot & System of Record Own HubSpot as both our CRM and our marketing automation platform, the company's system of record Design an interface an SDR can act on without guessing, and reporting leadership can read without asking you to pull it Support Sales and CS through a clear intake process, while marketing stays your primary customer and the VP of Marketing sets your priorities Curate the right tools around HubSpot, and apply AI wherever it removes a manual step How You'll Work Partner with marketing leadership, demand gen, field and events, content, and the SDR team, not as a ticket queue but as an owner who joins planning early and says so before launch when a plan won't track cleanly Run AI across your own operational work: data cleanup and enrichment, list building, workflow QA, reporting summaries, documentation, and internal tooling. A working practice matters more here than an interest in building one Report to the VP of Marketing, and carry the strategy for the funnel model itself, not just its execution What It Takes 4 to 7 years in marketing operations or revenue operations at a B2B company with an enterprise sales motion Deep HubSpot expertise with the platform as the system of record, across both CRM and marketing automation. Running HubSpot next to a separate CRM is a different skill set and doesn't substitute for this You've designed a lead lifecycle and routing model from a blank page, not just maintained one someone else built Command of attribution modeling and its tradeoffs, including a clear account of what any given model fails to capture Hands-on with tracking: tag manager, pixels, UTM standards, and conversion tracking across web and campaign surfaces Comfortable in the data itself: enrichment, deduplication, list operations, and analysis in spreadsheets or SQL Specific AI workflows in daily use, with a clear story of what you automated and what it replaced Clear writing, and the judgment to push back on a request that would break the model