People Insights & Total Rewards Manager, Omnidian

• You’ve worked inside an HR or People function — comp, total rewards, or people analytics — and you’re fluent in how comp bands, equity, and workforce data actually work, not just how to query them
• You don’t need to have designed a total rewards program, but you’ve seen one built well and have opinions on how compensation and equity structures should come together
• Handed a messy dataset split across systems that don’t talk to each other — or a system you’ve never worked in before — you get curious, not annoyed, and dig in until you find the fix
• You care how comp, engagement, recruiting, and workforce data connect to business performance — not just how to pull the numbers
• You use AI tools daily to speed up the parts of People analytics that used to take hours — cleaning messy HRIS exports, drafting first-pass comp narratives, summarizing survey verbatims — and you know when to trust the output and when to double-check it
• 5–9 years in people analytics, total rewards, or a related HR discipline
• Exposure to equity administration and cap table modeling, with a clear perspective on how comp and equity structures should be designed
• Experience with compensation benchmarking tools and methodology (Pave, Radford, Mercer, or comparable)
• Comfort working across HR systems (an HRIS, an engagement/performance platform, an ATS) as a power user, without needing formal administrator ownership
• Advanced Excel or Google Sheets skills
• Excellent written English and the judgment to work autonomously across time zones

Desirable

• Build reliable reporting across engagement, recruiting, workforce planning, and organizational health
• Act as the technical eye on Rippling, Lattice, Carta, and Pave — spotting what’s underused or inefficient, fixing it, and turning what the data reveals into insights leaders and the People team can act on
• Lead total rewards analysis, including equity and cap table forecasting, compensation benchmarking, and the relationship between rewards and engagement
• Partner with People Business Partners, Recruiting, and L&D to turn findings into workforce plans, hiring priorities, and program improvements
• Use AI tools to work efficiently while validating results and maintaining data quality
• Act as the technical eye on Rippling, Lattice, Carta, and Pave to identify what’s underused or inefficient and make the needed edits to ensure our People tools are maximized and producing valuable organizational insights
• Proactively surface emerging risks and opportunities, from narrowing talent pipelines to reward programs that may need adjustment