Staff Engineer, Backend, Revenue

Ovii's Interpretation of the Role

Zapier seeks a Staff Engineer to own the technical vision and architecture of its Revenue Zone, delivering finance‑grade billing, pricing, and payment systems at scale. The role blends deep backend expertise with cross‑team leadership to enable rapid monetization changes while guaranteeing revenue accuracy.

Role Snapshot

• Technical leader for revenue platform
• Architect of billing, pricing & payments
• Cross‑functional stakeholder partner
• Mentor and technical bar‑raiser
• Remote‑first, global scope

Must-Have Requirements

• Billing systems
• Subscription management
• Payment processing
• Pricing & entitlement models
• API design
• Performance optimization (profiling, caching, DB tuning)
• AI coding tool usage
• Observability & reliability engineering
• Billing/payments/monetization domain
• API/platform engineering
• Performance optimization at scale

Work Setup

• Location: San Francisco, United States
• Work mode: REMOTE
• Remote scope: GLOBAL
• Remote countries: United States, NAMER
• Employment type: Full-Time

Eligibility Gates

• Visa sponsorship: unknown

Not Specified in JD

• Visa sponsorship
• Salary range
• Relocation
• Notice period
• Coding test

What You'll Likely Work On

• Define and evolve the long‑term architecture for the Revenue Zone, balancing short‑term delivery with durability and extensibility
• Lead high‑impact initiatives that decouple billing from account systems, build configurable pricing infrastructure, and reduce technical debt
• Own the resilience, observability, and correctness of billing, pricing, payments, checkout, and entitlement services
• Guide API design, data modeling, caching strategies, and system‑wide performance optimizations
• Partner with Product, Design, Finance, and Sales to shape monetization strategy and drive ARR growth
• Mentor engineers, conduct code reviews, and champion documentation and developer experience

Good Fit If You Have

• Enjoys solving ambiguous, high‑impact problems with data‑driven trade‑offs
• Thrives in a finance‑grade, correctness‑critical environment
• Comfortable using AI tools while maintaining rigorous human oversight