Revenue Operations Leader

Overview

You'll architect revenue operations at Scytale as if it's a product team, not a support function. Instead of fielding tickets from Sales/Marketing/CS, you'll own a roadmap of revenue systems and ship AI-native workflows, internal tools, and data infrastructure that eliminates manual work across GTM teams.

Role Snapshot

Company Context

Stage: Likely Series A/B (150 employees, "hypergrowth" language suggests recent funding)

Size: 150 employees

Growth: Aggressive hiring, describing themselves as "fast-scaling" and "AI-first"

Market Position: Trust & compliance software - competitive space with established players, positioning around AI differentiation

GTM Reality

Your Scope:

• Revenue infrastructure across Sales, Marketing, Partnerships, and CS
• Building AI-native workflows (not just implementing vendor tools)
• Creating internal tools that GTM teams actually use
• Data systems that drive execution, not just reporting dashboards

Current State (likely):

• 150 people means they're past startup chaos but systems aren't mature
• Probably have basic CRM/MarTech stack but lots of manual processes
• GTM teams likely frustrated with data quality, lead routing, reporting lag
• AI-first positioning suggests they want to lead with automation, not bolt it on

What You'll Actually Do

Time Breakdown

Building/Shipping (40%) | Stakeholder Alignment (30%) | Data/Analysis (20%) | Vendor Management (10%)

Key Activities

• Product Roadmap Management: You maintain a 6-12 month roadmap of revenue systems to build/ship. Weekly prioritization with exec team on what automates the most manual work or unblocks the most revenue.
• AI Workflow Development: You design and implement AI-native workflows—think automated lead enrichment, AI-assisted deal scoring, automated follow-up sequencing. You're prototyping with GPT APIs, not just turning on vendor AI features.
• Internal Tool Building: You scope and ship internal tools (likely low-code or working with eng resources). Examples: custom partner referral portal, automated QBR generation for CS, sales comp calculator that updates in real-time.
• Cross-functional Systems Design: You run working sessions with Sales, Marketing, CS leaders to map their workflows and identify what should be automated, consolidated, or eliminated. You're designing how revenue actually flows.
• Data Infrastructure: You own the data model for how revenue data connects across systems. Fixing lead-to-account matching, building unified customer views, ensuring data quality at the source.
• Stakeholder Management: Constant alignment with VP Sales, CMO, VP CS on priorities. Lots of "why isn't this done yet" conversations when systems work is invisible until it ships.

The Honest Reality

What's Hard

• Building vs. Firefighting: Everyone wants their issue fixed now. You have to ruthlessly prioritize long-term systems work over urgent one-off requests. GTM leaders will push back when you say "that's not on the roadmap."
• Ambiguous Scope: "AI-native workflows" could mean anything. You need to define what this actually means in practice and set realistic expectations about what AI can/can't do.
• Resource Constraints: At 150 people, you probably don't have a big team. You're doing IC work, managing a small team, and trying to scale yourself through automation.
• Change Management: Getting sales reps to use new tools is hard. You'll build elegant systems that people ignore if the change management isn't there.
• Vendor vs. Build Decisions: Constant tradeoffs between buying tools vs. building custom. Buying is faster but creates tech debt and integration nightmares. Building takes longer but fits exactly.
• Measuring Impact: Hard to quantify "we saved 10 hours/week per rep" when revenue results are multi-variant. You need to get good at showing efficiency metrics that executives care about.

What Success Looks Like

• GTM teams adopt your tools without being forced: Reps actually use the internal tools you ship because they make their jobs easier
• Manual work measurably decreases: You can point to specific workflows that used to take hours and now take minutes
• Revenue per GTM headcount increases: The company grows revenue without linearly adding GTM headcount because systems scale
• AI becomes infrastructure, not novelty: AI features ship routinely as part of workflows, not as one-off experiments
• Data drives decisions: Leadership makes GTM decisions based on your data systems, not gut feel or spreadsheets

Who You're Supporting

Internal Stakeholders:

• VP Sales / CRO
• CMO / VP Marketing
• VP Customer Success
• Partnerships Lead
• CEO (probably involved given company size)

What They Care About:

• Sales cares about: Pipeline visibility, accurate forecasting, reducing admin time, faster deal cycles
• Marketing cares about: Attribution, lead quality, campaign ROI, ABM infrastructure
• CS cares about: Renewal risk visibility, expansion pipeline, customer health scoring, reducing manual account updates
• CEO cares about: Revenue efficiency metrics, scalability, data-driven decision making, not hiring GTM bloat

Requirements

• Product thinking: You've built systems with roadmaps and shipped measurable improvements, not just maintained existing tools
• AI fluency: You understand how to build with LLMs, not just use ChatGPT. You can scope AI workflows and know what's realistic vs. hype
• Technical enough: You can write SQL, work with APIs, maybe script in Python. You don't need to be an engineer but you need to spec technical work clearly
• RevOps experience at scale: You've been through 50→200+ person growth and know what breaks at each stage
• Stakeholder management: You can say no to powerful people and defend your roadmap. You can manage up to execs who want everything now
• Hands-on builder: This isn't pure strategy. You're in HubSpot/Salesforce building workflows, writing Zapier automation, testing tools yourself
• Change management: You've successfully gotten teams to adopt new systems and can run internal launches like product launches