Head of Product Engineering, HR Total Rewards - Director

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 , accountable for defining the product vision and building, scaling, and operating a high-performing organization of approximately 50 resources across multiple autonomous pods and enabling capability teams. This 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. The leader must remain deeply grounded in hands-on product and engineering judgment — including the expectation to stay actively involved in coding, code review, prototyping, AI -assisted development , internal developer platforms, vendor platforms, and modern engineering practices — while expanding accountability to product vision, organizational design, talent enablement, stakeholder management, budget ownership, vendor governance, and delivery outcomes at scale. This is not a traditional “manage from a distance” role. You are expected to stay close enough to the work to write and review code when needed, build prototypes, validate AI-generated output, understand product tradeoffs, assess technical risk, influence architectural direction, and improve delivery flow — while leading through leaders, creating clarity across teams, and building the systems that allow autonomous pods to move faster, safer, and with greater accountability. You set the standard for how the organization works, not just what it ships. You model the durable skills — Observant, Curious, Determined — and institutionalize them through hiring, coaching, rituals, governance, and leadership expectations . Success requires a leader who can operate fluidly across altitude: from senior executive stakeholder conversations , SaaS portfolio tradeoffs, workforce transformation strategy, and budget prioritization to product discovery reviews, architectural debates, AI-assisted delivery practices, vendor platform decisions, and talent development conversations with emerging leaders. What You Will Do 🧭 Lead the Pod

• Lead a ~ 50 -person product engineering organization across multiple autonomous pods, engineering leads, product engineers, and enabling capability teams — owning performance, delivery health, talent development, and organizational effectiveness
• Build the leadership system for the organization: clear goals, operating rhythms, delivery reviews, talent forums, retrospectives, and mechanisms that surface risks early without creating unnecessary process drag
• Create clarity across teams on priorities, decision rights, ways of working, engineering standards, and how AI-assisted delivery should be used responsibly to accelerate outcomes
• Represent the organization with senior business, product, technology, risk, finance, and HR stakeholders — translating strategy into delivery commitments and delivery realities back into executive decision-making
• Own workforce planning, hiring strategy, vendor/resource mix, succession planning, and budget tradeoffs in partnership with senior leadership and finance.
• Oversee a broad ecosystem of third-party SaaS applications, ensuring clear ownership, rationalized investment, strong vendor governance, secure integrations, operational resilience, and alignment to business outcomes
• Lead a holistic AI-enabled workforce transformation across the organization — moving AI from isolated tooling to a scaled operating capability embedded in how teams plan, build, test, support, learn, and make decisions 🧭 Set Direction & Shape Strategy
• Translate enterprise and business goals into a coherent product engineering strategy, investment roadmap, capability plan, and measurable team-level outcomes.
• Define and continuously evolve the product vision for Total Rewards technology — connecting employee, manager, HR, and business needs to a clear long-term direction, investment thesis, and measurable outcomes.
• Set the direction for how teams define problems, prioritize opportunities, validate hypotheses, and make tradeoffs across user value, technical feasibility, risk, cost, and speed.
• Identify patterns across pods and invest in shared platforms, reusable components, standards, and AI-enabled delivery practices that raise the productivity of the full organization.
• Build trusted executive relationships by communicating progress, risks, dependencies, and tradeoffs with clarity, transparency, and a strong bias toward action.
• Manage the portfolio with discipline — making explicit investment choices, managing capacity, tracking value realization, and ensuring budget is deployed against the highest-priority outcomes.
• Drive portfolio simplification across custom platforms and third-party SaaS solutions — reducing duplication, modernizing integration patterns, improving adoption, and ensuring technology spend is aligned to strategic value.
• Define the AI workforce transformation roadmap, including capability uplift, adoption measurement, responsible usage standards, change management, communications, and practical enablement for engineers, product managers, managers, and business partners. 🏗️ Architect for Speed & Safety
• Establish organization-wide architectural principles, engineering guardrails , security expectations, and delivery standards that enable multiple teams to move quickly without accumulating unsustainable technical or design debt.
• Govern the use of AI-generated code, designs, and architectural proposals across the organization — ensuring leaders and engineers understand when to trust, challenge, redirect, or reject AI output.
• Drive build-vs-configure-vs-orchestrate decisions at portfolio level , leveraging the internal developer platform , shared services, vendor capabilities, and AI agents appropriately.
• Partner with SaaS vendors and internal platform teams to shape roadmaps, influence product direction, manage escalations, and ensure third-party capabilities are integrated into a coherent product and data architecture. 🔍 Elevate Discovery & Validation
• Model and coach rigorous hypothesis-driven development — ensuring that speed of iteration is matched by quality of learning.
• Design experiments that generate high-signal feedback quickly, and establish the metrics that matter before building begins.
• Challenge assumptions at every stage — including your own. 🛠️ Lead Technical Validation
• Remain hands-on enough to write code, review pull requests, inspect critical technical decisions, validate high-risk AI-assisted outputs, challenge architectural assumptions, and coach leaders on technical quality.
• Maintain deep technical fluency across modern engineering practices, AI-assisted development, cloud-native architecture, security, resiliency, and data/platform patterns — without becoming a bottleneck for every decision.
• Set the engineering excellence bar across the organization, including code review expectations, testing strategy, observability, release governance, platform adoption, and responsible AI usage.
• Institutionalize responsible AI usage across the workforce through standards, training, reusable patterns, measurement, and feedback loops that continuously improve productivity, quality, and employee experience. 🌱 Develop the Team & the Model
• Build a strong leadership bench by coaching managers, product engineering leads, and senior practitioners — sharing mental models, raising standards, and creating clear paths for growth.
• Create a talent enablement strategy for the organization, including capability mapping, skill development, succession planning, AI fluency, career pathways, and targeted hiring plans.
• Continuously evolve the Product Engineer operating model — defining what should be standardized, what should remain autonomous, and how teams learn from ea