Revenue Systems Architect — Scale GTM & Pipeline

Visa Hunt; San Francisco (CA); On-site; USD 190,000 - 240,000; Full time

Benefits offered by this job

Salary that makes sense

Competitive equity

Generous PTO

Parental leave

Wellness stipend

Learning & Development

Team offsites

Sabbatical

SF HQ perks

Job summary

Firecrawl is seeking a Revenue Operations Lead to architect the revenue infrastructure from day one in San Francisco, shaping the CRM, funnel instrumentation, lifecycle systems, and real-time reporting. You’ll own the revenue-systems function end-to-end, collaborating with product and finance to drive go-to-market decisions.

You’ll need 4+ years in revenue operations or GTM systems with hands-on CRM ownership, data modeling skills, and a track record of building scalable revenue data layers.

Qualifications

• 4+ years in revenue operations or GTM systems with hands-on ownership of CRM architecture and revenue data.
• Ability to model, pull, and structure data and build trusted reporting.
• Experience designing end-to-end revenue systems at scale.

Responsibilities

• Own and architect our CRM as the source of truth for revenue data.
• Instrument the full funnel end to end with accurate attribution.
• Build lifecycle systems to move prospects and customers through stages.
• Own revenue tracking and reporting with real-time pipeline insights.
• Design revenue infrastructure to scale with ARR growth.
• Collaborate with VP of Revenue, product, and finance on go-to-market systems.

Skills

CRM architecture

Revenue data

SQL

Job description

Firecrawl is seeking a Revenue Operations Lead to architect the revenue infrastructure from day one in San Francisco, shaping the CRM, funnel instrumentation, lifecycle systems, and real-time reporting. You’ll own the revenue-systems function end-to-end, collaborating with product and finance to drive go-to-market decisions.

You’ll need 4+ years in revenue operations or GTM systems with hands-on CRM ownership, data modeling skills, and a track record of building scalable revenue data layers.