Revenue Systems/Operations Role

Overview

You build AI workflows and automation tools for Hightouch's sales team. The post shows an example: a Claude skill that auto-researches target accounts and drops intelligence updates in Slack. You'll spend time designing these systems, connecting data sources (ZoomInfo, internal tools, web search), and helping reps customize them for their territory. You report to Nikko, Head of Revenue Systems.

Role Snapshot

Company Context

Stage: Series C+ (503 employees, mature product)

Size: 503 employees

Growth: Listed as one of America's Best Startup Employers, actively building AI capabilities

Market Position: Composable CDP/data activation space - competing with Segment, mParticle, legacy CDPs. Marketing to data teams and marketing ops at mid-market to enterprise companies.

What You'll Actually Do

Time Breakdown

Building/Testing Tools (40%) | Rep Training/Support (25%) | Integration Work (20%) | Planning/Roadmap (15%)

Key Activities

• Designing AI workflows: Writing Claude prompts, setting up input/output structures, deciding which data sources to connect. The example in the post took a week to build—expect similar timelines for new tools.
• Integration work: Connecting systems like ZoomInfo, Slack, Glean (internal knowledge base), web search APIs. You're stitching together data sources and making them talk to each other.
• Rep enablement: Walking reps through setup ("upload your CSV, pick your Slack channel, choose signals to track"), answering questions, iterating based on feedback on what's useful vs noisy.
• Standardization: Building templates and default configurations so reps aren't starting from scratch, but still giving them flexibility to customize for their needs.

The Honest Reality

What's Hard

• Reps will want different things—someone selling to retail wants different signals than someone selling to tech companies. You're constantly balancing standardization with customization.
• AI outputs are inconsistent. You'll spend time filtering out noise, testing prompts, dealing with hallucinations or irrelevant results.
• Adoption is voluntary. If reps don't find it useful or it's too much work to set up, they won't use it. You're measured on whether people actually use what you build.
• You're working with bleeding-edge tech (Claude, AI agents). Things break, APIs change, and you're figuring it out as you go.

What Success Looks Like

• Reps actively use the tools you build without constant hand-holding
• Sales team says "this saved me 3 hours of manual research this week"
• Leadership sees measurable lift in meeting quality or conversion rates from better account intelligence

Who You're Supporting

Internal Customers:

• Account Executives (selling $50K-500K+ deals to marketing/data teams)
• SDRs/BDRs (prospecting into enterprise accounts)
• Customer Success (managing renewals and expansion)

What They Need:

• Account intelligence delivered automatically (org changes, tech stack, funding, news)
• Less time on manual research, more time on actual conversations
• Customizable alerts that fit their specific territory or segment

Requirements

• Experience with sales tools and APIs (Salesforce, ZoomInfo, Slack integrations)
• Comfortable writing prompts and testing AI outputs—you don't need to be an ML engineer but you need to understand how LLMs behave
• Know enough about the sales process to understand what reps actually need (not just what they ask for)
• Can code or script enough to connect systems and automate workflows (Python, JavaScript, or similar)
• Self-directed—this role involves building net-new things with limited examples to copy from