Compensation & Analytics Senior Analyst

Join Forter's Total Rewards team as a Compensation & Analytics Senior Analyst. In this role, you will own global compensation operations, manage job architecture, price new roles, advise stakeholders on compensation matters, and develop training materials. You will also utilize AI tools to automate analysis and reporting. The position offers benefits such as vision and dental coverage, fitness subsidies, 25 days of holiday, work-from-home opportunities, and health insurance.

- Own and manage global compensation operations and recurring programs end-to-end, including comp cycles, promotions, and bonus payouts. - Partner with HR, Finance, and functional stakeholders to support hiring, retention, and career growth through clear compensation guidance. - Price new and evolving roles using Radford and other survey data, maintain job matches, support the annual range refresh, and turn findings into clear recommendations.

- 3–5 years of progressive compensation experience, ideally in a tech or high-growth environment - Hands-on market pricing experience using Radford (or similar) and the ability to translate findings into clear narratives - Hands-on experience using AI tools (e.g., Claude, Glean, Gemini) to automate analysis, reporting, and documentation, and genuine curiosity about applying them to compensation work - Strong communication skills and the ability to partner with and influence People Partners, Talent Acquisition, Finance, and business leaders - Experience owning compensation programs such as salary reviews, promotion cycles, and bonus plans, with strong process ownership and attention to data integrity - Strong analytical skills: advanced Excel, compensation modeling, and workforce analytics. Experience with HRIS and comp planning tools (e.g., HiBob, Aeqium) is a plus - Background supporting multiple functions and global populations, with hands-on job architecture and leveling experience - Collaborative, solutions-oriented approach, with the ability to bring structure to ambiguity and make programs better over time