Overview
You're the Rev Ops person at a 265-person voice AI API company. Your job is to keep Salesforce clean, make CPQ work for increasingly complex deals, and find ways to automate manual work in the quote-to-cash process. You'll work directly with sales reps when they need quotes built, with finance on revenue recognition issues, and with leadership on pipeline reporting.
Role Snapshot
| Aspect | Details |
|---|---|
| Role Type | Revenue Operations Analyst |
| Sales Motion | Supporting both PLG/API self-service and enterprise sales |
| Deal Complexity | Ranges from simple API contracts to complex enterprise agreements |
| Sales Cycle | N/A - operations support role |
| Deal Size | N/A - supporting deals across spectrum |
| Quota (est.) | N/A - measured on process efficiency and data quality |
Company Context
Stage: Series B+ (funded, growth stage - 265 employees)
Size: 265 employees
Growth: Actively hiring across revenue functions, scaling their go-to-market
Market Position: Established player in voice AI APIs competing against AWS, Google, Azure speech services plus specialized vendors
GTM Reality
Pipeline Sources:
- Developer-led: Startups trying the API, upgrading from free tier
- Enterprise direct: Sales team working larger contracts with compliance requirements
- Platform/partner: Companies building voice AI features into their products
SDR/AE Structure: Likely hybrid - some inbound from product usage, some outbound to enterprise accounts
SE Support: Probably have sales engineers for technical demos and POCs
Competitive Landscape
Main Competitors: AWS Transcribe, Google Speech-to-Text, Azure Speech, AssemblyAI, Rev.ai
How They Differentiate: Accuracy, speed, and cost vs hyperscalers; enterprise features vs newer entrants
Common Objections: "Why not just use AWS?" and pricing transparency questions
Win Themes: Better accuracy, faster processing, more developer-friendly, enterprise-ready
What You'll Actually Do
Time Breakdown
Salesforce Admin (30%) | CPQ/Quoting (25%) | Reporting (20%) | Process Improvement (15%) | Ad-hoc fires (10%)
Key Activities
- Salesforce data cleanup: You spend a lot of time fixing duplicate accounts, updating contact roles, making sure opportunity stages are correct, and chasing reps to update their pipelines. The CRM is never as clean as you want it.
- Building quotes in CPQ: Sales reps come to you with custom pricing scenarios. You configure the quote in CPQ, make sure discounting is within policy, coordinate with legal on contract terms, and troubleshoot when the system breaks.
- Pipeline reporting: You pull weekly/monthly pipeline reports for leadership. This means reconciling data between Salesforce, billing systems, and usage data. You'll spend time explaining why numbers don't match between systems.
- Process automation: You're testing AI tools (ChatGPT, Claude, automation platforms) to reduce manual work. This could be auto-generating quote summaries, enriching lead data, or building chatbots for common sales questions.
- System integration: You work on connecting Salesforce to billing systems, product usage data, and marketing automation. Things break regularly and you troubleshoot API errors and data sync issues.
- Supporting deal desk: When deals get complex (multi-year, usage-based pricing, custom terms), you help structure them in the system and make sure finance can recognize revenue correctly.
The Honest Reality
What's Hard
- You're constantly putting out fires: Sales reps need quotes urgently, reports break right before board meetings, and integrations fail without warning. Your planned project work gets interrupted daily.
- Ambiguity is real: They say "thrives in ambiguity" because there's no playbook. You'll need to figure out how to price new products, handle edge cases, and build processes from scratch.
- Sales reps bypass your processes: No matter how good your system is, reps will find shortcuts or do things off-system. You spend time being the process police.
- API business creates unique complexity: Usage-based pricing, tier changes, overage handling - it's messier than pure SaaS. You're dealing with customers who might go from $500/month to $50K/month in usage.
- You're the middle person: Caught between sales wanting speed/flexibility and finance wanting control/accuracy. Both will be frustrated with you sometimes.
What Success Looks Like
- Quote turnaround time drops from days to hours
- Forecast accuracy improves - leadership trusts the pipeline numbers
- You eliminate 10+ hours/week of manual work through automation
- Sales reps stop asking "where do I find X?" because your systems are intuitive
- Revenue recognition issues decrease - finance isn't constantly asking you to fix deals
Who You're Supporting
Primary Stakeholders:
- AEs and SDRs: Need quotes built, data fixed, reports pulled
- Sales leadership: Need accurate pipeline visibility and forecasting
- Finance: Need clean data for revenue recognition and planning
- CS/Account Management: Need usage data integrated with Salesforce
What They Care About:
- Sales: Speed - they want quotes in hours, not days
- Leadership: Accuracy - they need to trust the numbers for board/planning
- Finance: Compliance - deals need to be structured correctly for rev rec
- Everyone: They want fewer systems and clicks to do their jobs
Requirements
- Deep Salesforce experience - you can build reports, workflows, and customize objects without constantly Googling
- CPQ experience required - you've configured products, pricing rules, and approval workflows
- Actually use AI tools daily - they mention this specifically, so you need to be comfortable with ChatGPT, Claude, automation platforms
- SQL or data analysis skills helpful - you'll be joining data from multiple systems
- Comfortable with ambiguity and building processes from scratch
- Can communicate technical concepts to non-technical people (explaining why systems work the way they do)
- API/usage-based pricing experience is a major plus - most SaaS ops people haven't dealt with consumption models