JD Case Study · Wealth-tech CRM
A Sr RevOps Engineer owning quote-to-cash and the GTM systems behind it for a fast-scaling advisor CRM: capture, enrichment, ICP scoring, routing, CPQ, billing, and the AI-native workflows that compress them. The Salesforce plumbing is the surface; the job is a revenue funnel the CTO can trust, and that is the engine I have already built in public. Deep Salesforce admin is my ramp, not my résumé.
One kind of number here. The analysis below runs on illustrative figures invented to show method. No Wealth-tech CRM data is used or implied; the operating logic is the deliverable.
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.
The first 90 days
The order matters. Nobody should change a system they haven’t reconciled, and nobody should present a verdict before the numbers under it can be trusted.
Days 1–30
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
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
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 · illustrative data
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.
Requirement → proof
Each line of the posting against something already built and running on this site. Where the fit is a ramp rather than a match, the row says so.
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.
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