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Liftoff is an AI-powered performance marketing platform for the mobile app economy, serving over 6,600 mobile businesses across 74 countries. The Sales Compensation Analyst role supports the administration and execution of Liftoff's sales incentive programs, working within the FP&A team in close collaboration with Sales, Finance, and RevOps.
Key responsibilities include supporting end-to-end sales compensation administration, including monthly and quarterly incentive calculations, payout preparation, and true-up processing. The analyst will validate source data inputs from CRM and finance systems, reconcile results, and flag discrepancies prior to payout runs. You'll assist with the operation and data management within sales compensation tools, including data entry, testing, and issue escalation.
Additional duties include maintaining accurate documentation for compensation plans and calculation logic, supporting audit and reconciliation processes to ensure data accuracy and compliance, and preparing routine attainment and earnings reports for Sales leadership and Finance partners. The role involves ad-hoc compensation analysis, scenario modeling, and exception handling requests, as well as supporting bonus plan calculations outside of sales incentives.
Over time, this person will build expertise in Liftoff's go-to-market motion and revenue lines, working toward full command of every active comp plan and quota assignment to become the trusted point of contact for GTM leadership on plan mechanics and attainment questions. The role offers opportunities to identify process inefficiencies and surface recommendations for improvement.
Requirements include a Bachelor's degree and 2+ years of experience in finance, compensation, or a closely related analytical role. Working knowledge of sales compensation administration and incentive plan mechanics is essential. Experience with sales comp tools (Performio, Everstage, Spiff, CaptivateIQ, or similar) is required, with ability to ramp up on new tools as needed. Strong Excel and/or Google Sheets skills are necessary, with comfort working with large datasets and performing reconciliations. The ideal candidate demonstrates high attention to detail, a structured and process-oriented approach, and ability to work with moderate autonomy while seeking guidance effectively on complex assignments.
