Principal Product Manager

Why UKG:  

At UKG, the work you do matters. The code you ship, the decisions you make, and the care you show a customer all add up to real impact. Today, tens of millions of workers start and end their days with our workforce operating platform. Helping people get paid, grow in their careers, and shape the future of their industries. That’s what we do.

We never stop learning. We never stop challenging the norm. We push for better, and we celebrate the wins along the way. Here, you’ll get flexibility that’s real, benefits you can count on, and a team that succeeds together. Because at UKG, your work matters—and so do you.

About the role

We are seeking a strategic and entrepreneurial Principal Product Manager to define, build, and scale a new compensation intelligence and benchmarking product. The product will use data from thousands of customers across multiple industries to help organizations understand payroll and labor-market trends, compare pay for common jobs, and make more informed compensation decisions.

This is a high-impact role for a product leader who can operate at the intersection of compensation strategy, workforce data, job architecture, analytics, AI, and enterprise SaaS execution. You will establish the product vision and roadmap, develop trusted benchmark methodologies, and guide cross-functional teams from early discovery through launch, adoption, and expansion.

The ideal candidate combines deep product leadership with strong data fluency and practical knowledge of compensation, payroll, or labor-market products. They are comfortable translating complex workforce datasets, statistical methodologies, and job-matching approaches into intuitive, credible, customer-facing experiences.

Key Responsibilities

Product Strategy & Market Leadership
• Define the vision, strategy, positioning, and multi-year roadmap for UKG’s compensation intelligence and benchmarking product.
• Identify the highest-value customer problems across compensation planning, pay equity, recruiting, retention, workforce planning, budgeting, and market competitiveness.
• Prioritize opportunities by balancing customer impact, market differentiation, data readiness, technical feasibility, trust, regulatory considerations, and commercial value.
• Develop a clear point of view on how UKG can differentiate through timely payroll and workforce data, broad industry coverage, and integration into customer workflows.
• Create business cases, packaging recommendations, and measurable product outcomes in partnership with strategy, finance, sales, and go-to-market teams.

Compensation Benchmarks & Workforce Insights
• Define benchmark experiences that show how employees are paid based on job, industry, geography, company characteristics, experience, skills, and other relevant factors.
• Develop insights into payroll and labor trends such as base pay, total cash compensation, overtime, premiums, incentives, hours worked, and changes over time where supported by the data.
• Ensure benchmark outputs are useful for both strategic analysis and operational decisions, from market pricing and pay-range design to recruiting offers and retention reviews.
• Partner with analytics and data science teams to establish methodologies for aggregation, normalization, weighting, outlier treatment, cohort construction, and trend analysis.

Job Architecture, Classification & Matching
• Define how the product represents job families, functions, levels, skills, experience requirements, and other attributes needed for accurate market comparisons.
• Partner with AI, data science, and domain experts to improve job matching, title normalization, skill extraction, leveling, and confidence scoring.
• Design customer workflows for reviewing, correcting, and governing job matches, including transparent explanations and human controls for ambiguous cases.
• Balance industry-specific job detail with a scalable cross-industry framework that supports portability and consistent benchmarking.

Data Product Quality, Trust & Governance
• Own product requirements for data coverage, quality, freshness, lineage, privacy, security, aggregation thresholds, and benchmark reliability.
• Define clear customer-facing indicators for sample size, confidence, recency, methodology, and limitations so users can interpret results responsibly.
• Partner with legal, privacy, security, data governance, and responsible AI teams to ensure the product protects customer and employee information and complies with applicable requirements.
• Establish ongoing quality measurement and feedback loops to improve job mappings, benchmark accuracy, customer trust, and product utility.