Sr. Analyst, Executive Compensation

Restaurant Brands International•Toronto, ON

11d•CA$85,000 - CA$100,000•Onsite

About The Position

The Sr. Analyst of Executive Compensation will support the design, governance, and execution of the Annual Bonus and Equity Programs that define how 2,300+ employees and executives across RBI's global brands are rewarded. This is a high-visibility, high-impact role at the intersection of HR, Finance, Strategy, and Corporate Governance – it is an exceptional development opportunity for candidates who know RBI's business and want to build deep expertise in executive compensation. Executive Compensation is a global function responsible for RBI’s: Annual Bonus Program: Design and execution of the Management by Objectives (MBO) program central to RBI’s performance and compensation philosophies. Equity Programs (incl. Bonus Swap): Design and execution of the Bonus Swap and Discretionary Equity programs that contribute to a significant portion of total direct compensation of senior leaders. Executive Compensation: Support Board and Leadership Team with recommendations on market competitiveness, trends, benchmarking, and offer preparation for VP+ population. Governance & Disclosure: Plan design and compensation decisions for RBI Board of Directors review and approval for all matters related to executive compensation and incentives; preparation of the Proxy Statement including Compensation Discussion & Analysis for investors. This role – Sr. Analyst, Executive Compensation – reports to the Manager, Executive Compensation & Incentive Design and is responsible for collaborating with senior leaders to execute on both bonus and equity programs for all RBI brands and regions. Annual budget for our incentive programs is approximately $200 MM in cash and equity incentives. The ideal candidate will have a thorough understanding of financial and non-financial performance metrics for RBI business units, experience in managing senior leaders and business stakeholders, and proven ability to build and improve processes.

Requirements

Superior analytical skills and advanced financial acumen with ability to develop and maintain complex financial models – advanced Excel skills.
Bachelor's degree in quantitative fields such as mathematics, finance, engineering, business, statistics, economics, or related field.
2-4 years of experience working in Compensation, Total Rewards, HR Analytics, Finance, or equivalent teams
High attention to detail, sound judgment, confidentiality, and ability to manage multiple priorities in a fast-paced, global environment.
Excellent verbal and written communication skills, with the ability to advise stakeholders and present analytics and reports clearly.
Deep understanding of how large, multi-brand organizations like RBI operate – ability to navigate complex org structures and translate business context into compensation program design and decisions.

Nice To Haves

Certified Compensation Professional (CCP) and/or Certified Executive Compensation Professional (CECP) designation, or progress towards certification, is an asset.
Familiarity with AI and automation tools, particularly ChatGPT and/or Claude.
Experience with compensation benchmarking databases (e.g. Radford, Mercer, WTW, Salary.com) and/or equity administration platforms (e.g. Solium, Fidelity).

Responsibilities

Understand and manage eligibility for the Annual Bonus Program (+2,300 corporate employees) and the discretionary and bonus swap program
Partner across all our brands and functions to identify relevant KPIs and targets; track progress throughout the year and provide summary reports to senior leaders.
Analyze compensation surveys, peer data, and other market intelligence to provide insights on program design.
Utilize HR technology, analytics tools, automation, and responsible AI-enabled solutions to streamline day-to-day compensation processes and improve back-end productivity while maintaining appropriate human review.
Manage compensation models for scenario planning, pay-for-performance analysis, and financial impact modeling.