Revenue Operations Analyst

Evlo AI • Full-time • Remote (Minneapolis, MN, US) • 1d ago

About The Role

The role sits at the intersection of sales, data, and systems — owning the pipelines, dashboards, and process design that keep revenue teams running on accurate, actionable information.

You will work across Salesforce, marketing automation, and the BI stack to diagnose funnel issues, improve forecast reliability, and ship operational improvements that directly impact quota attainment and go-to-market efficiency.

Key Responsibilities

Own and administer the Salesforce instance: manage objects, validation rules, and workflows, and partner with Sales Systems on larger configuration changes
Build and maintain revenue dashboards in Looker/Tableau covering pipeline coverage, conversion rates, sales velocity, and forecast accuracy
Analyze funnel and territory performance to identify inefficiencies, model scenarios, and present recommendations to sales and finance leadership
Support the weekly and quarterly forecasting process: audit pipeline hygiene, flag data-quality issues, and reconcile CRM data against the ERP and finance systems
Design and document RevOps processes — lead routing, opportunity stages, handoffs to CS — and drive adoption across GTM teams
Scope, test, and roll out tooling changes across the revenue tech stack (Salesforce, Outreach, Gong, Marketo) with clear change management
Partner with Finance and Data Engineering on quota setting, commissions modeling, and definition alignment across systems

What We Are Looking For

3-6 years in revenue operations, sales operations, business analysis, or a comparable analytics-heavy role
Advanced Salesforce skills (reports, dashboards, flow, data model) — admin certification a strong signal
Proficiency with SQL and at least one BI tool (Looker, Tableau, or Power BI)
Demonstrated ability to turn messy, multi-source data into clear recommendations and executive-ready narratives
Experience supporting forecasting, pipeline management, or quota/commissions processes
Bachelor's degree in a quantitative field (economics, statistics, finance, or similar) or equivalent practical experience
Bonus: Python or R for ad-hoc analysis; experience with dbt or a modern data stack; familiarity with GTM planning tools like Clari or BoostUp