Head of Product Engineering, HR Total Rewards - C15 - Director - C15 - NEW YORK

About the Role

The Head of Product Engineering, HR Total Rewards is a senior product and engineering leader reporting directly to the Head of HR Technology. This role is accountable for defining the product vision, building, scaling, and operating a high-performing organization of approximately 50 resources across multiple autonomous pods and enabling capability teams. The role spans custom-built product engineering, oversight of dozens of third-party SaaS applications, and the end-to-end transformation of how the workforce uses AI to improve productivity, quality, and decision-making.

Responsibilities

• Lead the Pod:
  • Lead a ~50-person product engineering organization across multiple autonomous pods, engineering leads, product engineers, and enabling capability teams.
  • Own performance, delivery health, talent development, and organizational effectiveness.
  • Build the leadership system: clear goals, operating rhythms, delivery reviews, talent forums, retrospectives, and risk management mechanisms.
  • Create clarity on priorities, decision rights, ways of working, engineering standards, and responsible AI-assisted delivery.
  • Represent the organization to senior stakeholders, translating strategy into delivery commitments and vice versa.
  • Manage workforce planning, hiring, vendor/resource mix, succession planning, and budget tradeoffs.
  • Oversee third-party SaaS applications, ensuring ownership, investment rationalization, vendor governance, secure integrations, and alignment with business outcomes.
  • Lead AI-enabled workforce transformation, embedding AI into planning, building, testing, support, learning, and decision-making.
• Set Direction & Shape Strategy:
  • Translate enterprise goals into product engineering strategy, investment roadmap, and measurable outcomes.
  • Define and evolve the product vision for Total Rewards technology.
  • Set direction for problem definition, opportunity prioritization, hypothesis validation, and tradeoffs.
  • Identify patterns and invest in shared platforms, reusable components, and AI-enabled practices.
  • Build executive relationships through clear communication of progress, risks, and tradeoffs.
  • Manage portfolio discipline, making investment choices, tracking value, and aligning budget with priorities.
  • Drive portfolio simplification and modernization of integration patterns.
  • Define the AI workforce transformation roadmap, including capability uplift, adoption, and enablement.
• Architect for Speed & Safety:
  • Establish architectural principles, guardrails, security expectations, and delivery standards.
  • Govern AI-generated code, designs, and proposals, ensuring responsible usage.
  • Drive build-vs-configure-vs-orchestrate decisions, leveraging internal platforms, shared services, and AI agents.
  • Partner with SaaS vendors and internal teams to shape roadmaps and ensure coherent architecture.
• Elevate Discovery & Validation:
  • Model and coach hypothesis-driven development, ensuring quality learning.
  • Design experiments for high-signal feedback and establish key metrics.
  • Challenge assumptions at every stage.
• Lead Technical Validation:
  • Remain hands-on with coding, code review, prototyping, and technical validation.
  • Validate AI-generated outputs and coach leaders on technical quality.
  • Set engineering excellence standards, including code review, testing, observability, and responsible AI usage.
  • Institutionalize responsible AI usage through standards, training, and feedback loops.
• Develop the Team & the Model:
  • Build a strong leadership bench through coaching and development.
  • Create a talent enablement strategy, including capability mapping, skill development, and succession planning.
  • Evolve the Product Engineer operating model, balancing standardization and autonomy.
  • Shape cross-functional ways of working, ensuring the organization is a strategic partner.

Qualities and Skills

• Durable Skills — Non-Negotiable:
  • Observant: Anticipate issues, read data and situations proactively.
  • Curious: Seek challenging perspectives, update mental models based on evidence.
  • Determined: Drive outcomes, navigate ambiguity with clarity and conviction.
• Organizational Leadership — Required:
  • Experience leading multi-team product engineering organizations.
  • Ability to build high-trust leadership teams and coach through ambiguity.
  • Proven talent attraction, development, and retention.
  • Experience managing stakeholders, budgets, and complex SaaS portfolios.
  • Ability to lead enterprise-wide transformation programs, especially AI adoption.
• Technical Fluency — Required:
  • Hands-on experience with coding, prototyping, and technical validation.
  • Ability to review and validate AI-generated code across languages.
  • Strong understanding of system design, cloud-native architectures, and CI/CD practices.
  • Proficiency with AI coding tools and their limitations.
  • Experience with SaaS platform architecture and governance.
• Product & Design Fluency — Required:
  • Proven ability in hypothesis-driven product development.
  • Experience defining product strategy in ambiguous environments.
  • Ability to articulate a compelling product vision and align stakeholders.