OVERVIEW

• Remote friendly
• We are seeking a 4 month engagement to cover a leave on the compensation team at Instacart, ideally starting in late November/early December
• Open to experience levels at the Sr Comp Analyst to Compensation Partner level
• This role will report to our Senior Director, Compensation, and will have the unique opportunity to work across multiple aspects of compensation programs spanning executive, equity, and broad-based.

ABOUT THE JOB

• Support our executive and broad-based compensation benchmarking efforts
• Work with the HR Tech/Operations teams to update our systems with current data and compensation changes; audit Workday positions, compensation, and related workforce data on a recurring basis
• Work with the legal and stock admin teams to prepare monthly equity approval lists
• Play a key role in delivering Instacart’s annual review process with a focus on: tool support, data analysis, reporting and the development of education and communication materials.
• Develop compensation reports and tools to inform compensation program decisions, and to forecast and analyze compensation outcomes
• Produce ad hoc analyses and work on one-off projects and programs related to Total Rewards

ABOUT YOU

• 3 to 8 years of experience in compensation in a professional environment; in-house compensation or consulting experience preferred
• Advanced Excel/Google Sheets skills are required to analyze data and create insightful models and reports, experience with dashboard visualization and SQL preferred
• Ideal candidate is a self-starter who can manage projects independently, but also likes working with a tight-knit and supportive team
• Ability to interact with all levels of employees with a positive roll-up your sleeves attitude
• Thrives in a dynamic working environment with changing work priorities
• Experience using and submitting market data from Radford and/or Mercer
• Experience with executive compensation practices and gleaning insights from proxy reports and tools like Compensia and Equilar is strongly preferred