Manager, FP&A - R&D

BeyondTrust

About The Position

BeyondTrust is seeking an experienced FP&A business partner to support the CTO and Engineering organization, as well as the CCO organization (Professional Services, Support, Training, and Customer Success). This Manager-level individual contributor role focuses on R&D investment and the direct and indirect costs associated with delivering and supporting products. The core of the FP&A approach is to understand past performance, analyze its significance, and guide future business decisions. A key aspect of this role involves a deep dive into cloud infrastructure economics, moving beyond basic spend analysis to understand how architectural decisions, product usage, customer behavior, capacity, and technical choices impact costs and gross margin. The individual will collaborate with Engineering and Tech/FinOps to identify opportunities for economic improvement. Additionally, the role will support productivity and operating leverage analysis across R&D and the CCO organization, linking investments and capacity to business outcomes and scalability.

Requirements

Demonstrated, hands-on depth in SaaS cloud infrastructure economics, including consumption drivers, unit economics, scalability, and gross-margin implications.
Approximately 5–8+ years of progressive experience in FP&A, strategic finance, finance analytics, FinOps, or a related field.
Experience in a $300M–$1B PE-backed or pre-IPO enterprise B2B SaaS company, or a highly comparable environment.
Experience partnering with Engineering, Technology, Product, or Tech/FinOps leaders.
Strong understanding of SaaS gross margin and cost-to-serve economics.
Exceptional financial modeling and analytical skills, including the ability to work through complex or imperfect operational data.
Ability to independently move from analysis to insight to a clear point of view and recommendation.
Technical curiosity and sufficient fluency to engage credibly with Engineering and investigate beyond the initial explanation.
Strong communication and influencing skills, with the ability to work effectively with senior leaders in an individual contributor capacity.

Responsibilities

Serve as a key finance partner to the CTO and Engineering organization, providing financial and operational insight into R&D investment, resource allocation, productivity, and decision-making.
Drive a deeper understanding of cloud infrastructure economics by partnering with Engineering and Tech/FinOps to connect product and customer usage, architecture, capacity, and consumption to unit economics and gross margin, identifying opportunities for efficiency and scalability.
Apply a productivity lens to R&D investment, helping leaders understand resource and capacity deployment, supported outcomes, and areas for operating leverage improvement.
Develop business cases and scenario analyses to inform investment priorities and technical/operating decisions.
Partner with CCO leadership across Professional Services, Support, Training, and Customer Success.
Develop a clear understanding of the operational drivers of Services margin, capacity, utilization, staffing mix, and cost to serve.
Apply a productivity lens across customer-facing teams, connecting resources and capacity to business and customer outcomes.
Identify opportunities to improve efficiency, scalability, and operating leverage while balancing customer needs.
Own the budget, forecast, and performance outlook for the supported organizations, ensuring a clear and reliable view of results, risks, and opportunities for Finance.
Connect business activity and operational drivers to financial outcomes, including gross margin, cost to serve, and R&D investment.
Contribute to product P&Ls and product-level profitability views by linking R&D investment, infrastructure costs, and customer-delivery costs to product economics.
Bring insights from the business back into the broader FP&A organization to inform company-level forecasts, planning, and management reporting.
Explain performance drivers, assess their implications, and recommend appropriate actions.
Continuously improve models, data, and processes to dedicate more time to analysis and decision-making.