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Revenue Operations Analyst

Perplexity · San Francisco

full-timeLead

About the Role

At Perplexity, we're changing how the world finds answers. In 2026, we launched Computer, the defining product for the new era of agentic AI. Millions of people now use Perplexity to transform knowledge into action, and our revenue operations team builds the processes and systems that help bring those products to businesses around the world.

We're looking for a Revenue Operations Analyst who wants to do more than maintain a CRM or produce reports. This is an early, high-impact role where you'll work at the forefront of process, execution, and systems for our enterprise go-to-market organization. You will help determine how demand is attributed, how leads and accounts are routed, how opportunities move through the funnel, and how the business measures performance.

You will work directly with Sales, Marketing, Customer Success, Finance, Analytics, and Enterprise Systems to turn strategy into clean day-to-day execution. We give our operations team the same AI tools we build for everyone else, and we expect you to use them to eliminate manual work, identify issues, and improve how the revenue organization operates.

What you'll do:

  • Own and improve the day-to-day processes that move leads, accounts, contacts, and opportunities through the revenue funnel

  • Maintain accurate attribution across inbound, outbound, product-led growth, partner, referral, and event motions

  • Run and improve lead, account, and opportunity routing, including territory rules, ownership logic, rules of engagement, and exception handling

  • Build and maintain Salesforce reports, dashboards, fields, flows, validation rules, and other process configuration

  • Support pipeline reviews and forecasting by improving opportunity hygiene, stage accuracy, close dates, next steps, and follow-through

  • Build reporting across funnel conversion, pipeline health, source performance, rep productivity, and operational SLAs

  • Investigate and resolve data quality issues across Salesforce, Snowflake, marketing systems, sales engagement tools, and product usage data

  • Use Computer and other AI tools to automate research, enrichment, routing, pipeline inspection, quality assurance, documentation, and repetitive operational work

  • Partner with Sales Systems Engineering on larger integrations and technical projects, while owning business requirements, testing, rollout, and adoption

  • Document processes, train users, and provide fast operational support to sales representatives and front-line leaders

Qualifications:

  • 3+ years of experience in Revenue Operations, Sales Operations, Marketing Operations, Business Systems, or a similar role at a high-growth B2B company

  • Hands-on Salesforce experience, including reports, dashboards, data models, flows, validation rules, and process configuration

  • Strong SQL and analytical skills, with the ability to pull your own data, reconcile discrepancies, and translate findings into action

  • A strong understanding of B2B sales processes, including attribution, routing, account ownership, opportunity stages, pipeline inspection, and forecasting inputs

  • Sharp troubleshooting instincts and the ability to trace an operational problem across process, data, systems, and user behavior

  • A track record of leading operational projects from problem definition through rollout with minimal supervision

  • Clear communication, strong attention to detail, and a genuine interest in using AI to automate away busywork

In information theory, perplexity is a measure of uncertainty for a discrete probability distribution. The perplexity of a fair coin toss is 2, and that of a fair die roll is 6; and generally, for a probability distribution with exactly N outcomes each having a probability of exactly 1 / N, the perplexity is simply N. But perplexity can also be applied to unfair dice, and to other non-uniform probability distributions. It can be defined as the exponentiation of the information entropy. The larger the perplexity, the less likely it is that an observer can guess the value which will be drawn from the distribution.

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