Incentive Compensation Systems Manager | United States | Remote

Grafana Labs , the company behind the open observability cloud, is founded on the principles of open source, open standards, open ecosystems, and open culture. Grafana Cloud, our fully managed observability platform, is flexible and built for scale. With Grafana Cloud's actually useful AI, organizations can see, understand, and act on all their disparate data to move at the speed of their ambitions. Today, more than 35 million users and 7,000+ customers – including Anthropic, Bloomberg, NVIDIA, Microsoft, and Salesforce – trust Grafana Labs to ensure reliability of their applications and systems, resolve incidents quickly, and optimize their telemetry to reduce noise and cost. We are a 100% remote company with 1,600+ team members across 40+ countries, and we’re backed by leading investors including Lightspeed Venture Partners, Sequoia Capital, GIC, Coatue, J.P. Morgan, CapitalG, and Lead Edge Capital. Learn more at and follow us on LinkedIn and X. We’re scaling fast and staying true to what makes us different: an open-source legacy, a global collaborative culture, and a passion for meaningful work. Our team thrives in an innovation-driven environment where transparency, autonomy, and trust fuel everything we do. You may not meet every requirement, and that’s okay. If this role excites you, we’d love you to raise your hand for what could be a truly career-defining opportunity. The Opportunity: We are looking for an Incentive Compensation Systems Manager to join our Incentive Compensation (IC) team and drive the systemization, automation, and continuous improvement of our commission operations. This is a high-impact, individual contributor role with end-to-end ownership, reporting to the Senior Manager, Incentive Compensation. To be successful, you should look forward to operating in an unstructured, fast-paced, remote-first environment where you will identify gaps in existing processes, replace manual workarounds with scalable systems, and bring greater predictabilit