JD Case Study · Wealth-tech CRM
Sr RevOps Engineer — GTM Systems & Quote-to-Cash
A Sr RevOps Engineer owning quote-to-cash and the GTM systems behind it for a fast-scaling CRM for financial advisors — capture → enrichment → ICP scoring → routing → opportunity → CPQ → billing, plus the AI-native workflows that compress it. The Salesforce/CPQ plumbing is the surface; the real job is a revenue funnel the CTO can trust, and that is the engine I have already built in public. I will be straight about the one gap — deep Salesforce admin depth is my ramp, not my résumé — and just as straight about the leverage I bring on day one.
What they’re actually buying
Strip away the tool names and this role is one promise: turn a messy GTM stack into a measurable, automated path from lead to recognized revenue — and make the data underneath it trustworthy enough that the Cofounder/CTO runs the business on it. Salesforce and CPQ are the plumbing; the value is the funnel logic, the routing SLAs, the data quality, the experimentation that tunes it, and the LLM-powered workflows that take the manual work out. That is RevOps engineering pointed at the same trade-off every revenue system manages: every lead you capture against every dollar that actually reaches cash.
How I’d own it — first 90 days
Days 1–30
Map the revenue plumbing
Document the quote-to-cash path end-to-end — CRM → CPQ → billing — and inventory every integration point and where it silently breaks. Baseline the lead-to-revenue funnel and its data-quality gaps. Meet GTM, Engineering, and Finance and find the leaks they already feel.
Days 31–60
Instrument the funnel & SLAs
Stand up a lead-to-revenue dashboard — capture → enriched → routed → opportunity → quote → billed — with data-quality and routing-SLA monitors so leaks are visible instead of anecdotal. Ship the first reliability fix on a brittle CRM→CPQ→billing integration point.
Days 61–90
Ship an AI-native workflow + an experiment
Deploy one LLM-powered workflow behind a guardrail — lead enrichment / ICP scoring or quote assembly — and A/B a routing or scoring change with the revenue impact read out in dollars. Set the data-governance and experimentation cadence the stack will run on as it 4×s.
Signature analysis: the lead-to-revenue funnel
Where RevOps pays for itself: the steepest, cheapest-to-fix leak sits between capture and opportunity — stale data, slow routing, no ICP score. Tighten enrichment, scoring, and routing SLAs and the whole funnel lifts without a dollar more of spend. Downstream, a reliable CRM→CPQ→billing path stops revenue leaking on the way to cash. Both are systems-and-data problems, not sales-headcount problems — which is exactly why this seat exists.
Illustrative GTM funnel — the lead-to-revenue and quote-to-cash path this role owns. The same full-funnel + experimentation engine I built on a lease-to-own book, pointed at a GTM stack: leads instead of applications, quotes instead of fundings, the same measurable path to cash.
Requirement → proof
The same engine
The engine behind this whole portfolio — full-funnel capture→revenue, experimentation with a guardrail, exec scorecards, and the data quality underneath — is exactly what a RevOps function runs on. I built it on a lease-to-own book; here it points at a GTM stack. The merchandise changes, the measurable path to cash does not. The Salesforce/CPQ plumbing I would ramp into fast; the revenue logic I already speak fluently.
See the revenue funnel I built →I will not pretend to be a ten-year Salesforce admin. But the part of this role that is genuinely hard to hire for — turning a messy GTM stack into a revenue funnel the CTO can trust, with AI-native workflows on top — is the work I do. Let’s talk about your quote-to-cash. — Paul Brown