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We believe learning is the key to unlocking opportunity, and our mission is to discover, empower, and celebrate the next generation of global expertise. Join us in shaping the future of learning and workforce development!

About the Role

Degreed is hiring a Senior Revenue Intelligence Analyst to own and drive a unified GTM data strategy supporting pre-sales, post-sales, and leadership with analytics and insights to run the business. This is an individual contributor role inside the global revenue operations team, reporting to the head of RevOps. It comes with regular visibility with senior leaders and the opportunity to make a meaningful impact for cross-functional teams.

In this role, you’ll drive revenue intelligence and insights with an AI-forward approach, enhancing business outcomes like retention and new ARR and improving our measurement of marketing ROI, forecasting accuracy, and predictive churn signals. You’ll own and build the dashboards and reporting behind important GTM decisions using our newly implemented BI tool (Hex), and tying data and performance back to pipeline, revenue, and retention. You’ll work with product and engineering to keep warehouse data clean and help validate the commercial data underneath it all, including ARR, quotes, order forms, and renewals.

Revenue Operations is a highly collaborative, positive team - we value new points of view, share our expertise, ask for help when we need it, and support each other to get the job done. Degreed is also changing how this function works with AI, from documentation to agents, and you’ll play an impactful role in how revenue operations continue to evolve with AI.

Key Skills

SQL and BI dashboarding: read and write SQL, and build BI dashboards that sales, marketing, and leadership use in regular decisions.
Revenue analytics: trace pipeline from top-of-funnel marketing through closed revenue, find where it falls out of the funnel, and explain what the numbers do and don’t show.
Executive communication: present analysis to senior leaders, and explain the numbers when they’re challenged.
Stakeholder ownership: run conversations with sales, marketing, and finance leaders on your own, without needing direction at each step.
Commercial data validation: check ARR, net new ACV, prorated terms, FX adjustments, and quote-to-order form alignment, and flag errors before they reach finance.
Data quality: judge whether warehouse views are clean and validated, and work with engineering to fix them when they aren’t.
Tool selection: decide when a report or dashboard belongs in Claude, Salesforce, or Hex, and explain the tradeoff.
Requirements translation: turn a business need into technical requirements that field operations, system architects, and engineers can build from.
Financial fluency: leverage strong working knowledge of revenue recognition principles, gross/net revenue retention (GRR/NRR), forecasting, and CPQ logic.
Ability to leverage AI tools and technologies: use AI (e.g. Claude, Gemini, CoPilot) as a partner in your daily work to accelerate QA, contribute to AI-ready documentation, expand our GTM context layer, and automate recurring work.

Key Responsibilities

Own the GTM data strategy across pre-sales and post-sales motions, so every team works from the same definitions and the same numbers.
Build the business-level dashboards, forecasts, and analyses that track pipeline from top-of-funnel marketing through closed revenue, and tie numbers back to top line financial metrics.
Own executive and board-level reporting for quarterly business reviews, and validate every figure before it goes out.
Build forecasts and function-specific reporting for marketing, sales, and services, and refine the assumptions behind them as results come in.
Improve our ability to surface early risk signals in new ARR, retention and churn.
Manage and prioritize reporting and dashboard requests from stakeholders inside or outside revenue (e.g. finance)
Build new views and datasets to meet business requirements. Partner with engineering to keep warehouse views clean and engage their resources when more complex support is needed.
Turn analysis into plain-language recommendations for leadership.
Validate commercial data that affects sales and finance reporting and forecasting, including ARR, net new ACV, prorated terms, FX adjustments, quote-to-order form alignment, and renewal details such as subscriptions, renewal values, and off-cycle changes.
Build AI-ready documentation, context, and data architecture for the team, and build agents that other people use.

Compensation

We are committed to fair and equitable compensation practices.The total pay range for this role is $130.000 - $150.000.
Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to: skill set, depth of experience, certifications, specific work location, and internal equity.