Senior Manager, Revenue Operations

Overview

You're the founding RevOps leader at a fast-growing AI enterprise software company in Toronto. You'll spend your days building out HubSpot, connecting systems, creating dashboards, and translating what the data is actually saying to Marketing, Sales, and CS leaders. This is a builder role—you're setting up infrastructure that doesn't exist yet, not optimizing what's already there.

Role Snapshot

Company Context

Stage: Likely Series A/B (fast-growing, building first RevOps function, selling enterprise)

Size: Unknown, but growing quickly enough to need dedicated RevOps

Growth: Actively hiring, described as "fast-growing" with enterprise traction

Market Position: Challenger in AI enterprise software space (crowded market, need to differentiate)

GTM Reality

Pipeline Sources:

• Unknown mix, but you'll need to instrument tracking for all of it
• Likely some inbound from AI/enterprise interest, some outbound enterprise motion
• Possibly partnerships given enterprise focus

SDR/AE Structure: Unknown—you'll need to figure out what's working

SE Support: Likely present given enterprise AI complexity

Your Job: Make sense of what's currently happening, then build the systems to scale it. Right now they probably have data in multiple places, inconsistent tracking, and leaders making decisions on gut feel.

What You'll Actually Do

Time Breakdown

HubSpot Admin (35%) | Reporting/Analysis (25%) | Cross-functional Meetings (20%) | Process Design (20%)

Key Activities

• HubSpot Configuration: Building workflows, custom properties, pipeline stages, integrations. You're in the tool daily fixing data issues, adjusting automations, and fielding requests from Sales/Marketing.
• Dashboard Creation: Building and maintaining reports that actually get used. Conversion rates, pipeline velocity, forecast accuracy, activity metrics. You'll rebuild these multiple times as you figure out what leaders actually need.
• Data Cleanup: Fixing duplicates, standardizing field usage, chasing down reps who don't log activities. This is tedious but critical. Bad data in = useless insights out.
• Cross-functional Translation: Sitting in meetings with Marketing, Sales, and CS explaining what the data shows and what it means for their strategy. Often mediating disagreements about attribution or what metric matters most.
• Tech Stack Evaluation: Researching and implementing AI-forward tools. Conversation intelligence, intent data, forecasting tools. You'll demo a lot of vendors and negotiate contracts.
• Process Documentation: Writing playbooks, creating training materials, standardizing definitions (what counts as a qualified lead? when does a deal move stages?).

The Honest Reality

What's Hard

• You're building from scratch: There's no documentation, no clean data, no established processes. You'll spend weeks just understanding what's actually happening before you can improve anything.
• Everyone wants something different: Marketing wants credit for pipeline, Sales wants simpler workflows, CS wants better handoffs, leadership wants accurate forecasts. You're constantly balancing competing priorities.
• Data quality is a grind: Reps don't log activities consistently. Fields are used wrong. Duplicates everywhere. You'll spend hours cleaning data that gets messy again next week.
• You're the bottleneck: Every dashboard request, every HubSpot question, every "can you pull this report" comes to you. You'll need to say no a lot and teach people to self-serve.
• AI tooling is immature: The "AI-forward tech stack" sounds cool but most tools overpromise. You'll implement things that don't work as advertised and need to rip them out.
• Building a team takes time: You're promised you'll hire eventually, but expect 12-18 months of being a team of one. Every project depends on your time.

What Success Looks Like

• Forecast accuracy improves: Sales leadership can actually trust the forecast within 10-15% instead of it being a guess
• Pipeline visibility: Everyone can see where deals are stuck and why, leading to better coaching conversations
• Faster decision-making: Marketing can see ROI by channel within days, not weeks. Sales leadership knows which activities correlate with wins.
• Clean handoffs: Leads don't fall through cracks between Marketing and Sales, or between Sales and CS
• You hire your first ops analyst: You've built enough that there's work to delegate

Who You're Supporting

Internal Stakeholders:

• VP/Director of Sales (your main partner, wants accurate forecasts and pipeline visibility)
• Marketing leader (wants attribution clarity and lead quality metrics)
• CS leader (wants clean handoffs and expansion pipeline tracking)
• Finance (wants revenue forecasting and bookings data)

What They Care About:

• Sales: Can I trust my forecast? Where are deals getting stuck? Which reps need help?
• Marketing: What channels drive real pipeline? Are we sending Sales quality leads?
• CS: Are we retaining customers? Where's expansion opportunity?
• Finance: Will we hit the number? What's our revenue trajectory?

Requirements

• Deep HubSpot Sales Hub expertise (not just "familiar with"—you need to know workflows, custom objects, reporting limits)
• Experience implementing AI/ML tools in a revenue context (conversation intelligence, intent data, predictive analytics)
• Strong SQL or similar data skills (you'll need to pull data from multiple sources and combine it)
• Track record building RevOps at a high-growth company (they need someone who's done this before, not learning on the job)
• Ability to work hybrid in Toronto (in office some days, remote others)
• Comfort with ambiguity (there's no playbook, you're writing it)
• Cross-functional influence skills (you have no authority over Sales/Marketing/CS but need to change how they work)