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 each other across the organization. 

• Shape cross-functional ways of working with business, product, design, risk, operations, finance, and engineering partners — ensuring the organization is viewed as a strategic partner, not just a delivery function. 

What We Are Looking For 

Durable Skills — Non-Negotiable 

These are the qualities we hire for first. They cannot be taught by a framework or acquired through a certification. 

Organizational Leadership — Required 

• Demonstrated experience leading a multi-team product engineering organization, including managers, senior practitioners, and cross-functional delivery groups. 

• Ability to build high-trust leadership teams, set clear expectations, coach through ambiguity, and hold leaders accountable for outcomes, quality, and team health. 

• Experience creating environments where teams can move quickly, challenge assumptions, surface risks early, learn openly, and continuously improve without fear or bureaucracy. 

• Comfort operating as an executive-level player-coach: close enough to product and engineering detail to improve decisions, but disciplined enough to scale through leaders and operating mechanisms. 

• Proven ability to attract, develop, retain, and enable talent across levels — including succession planning, capability building, performance management, and leadership development. 

• Experience managing stakeholders, budgets, capacity plans, hiring plans, vendor/resource mix, and delivery commitments in complex, matrixed organizations. 

• Experience overseeing a complex third-party SaaS portfolio, including vendor governance, contract and budget inputs, lifecycle management, platform rationalization, integration strategy, support models, and operational risk. 

• Proven ability to lead enterprise or organization-wide transformation programs, particularly those requiring behavioral change, workforce enablement, operating model change, and measurable adoption of AI-enabled ways of working. 

Technical Fluency — Required 

• Demonstrated ability and willingness to remain hands-on with coding, prototyping, debugging, and technical validation — using direct engineering contribution to improve decisions, not replace team ownership. 

• Ability to critically review and validate AI-generated code across multiple languages — identifying correctness issues, security vulnerabilities, and architectural anti-patterns. 

• Strong understanding of system design principles: scalability, resilience, data consistency, API design, and security boundaries. 

• Proficiency with AI coding tools (Devin AI, Claude Code, GitHub Copilot, Cursor) — including knowing their failure modes and limitations. 

• Experience with cloud-native architectures, containerization (Docker, Kubernetes), and CI/CD practices. 

• Ability to define and enforce testing strategies — unit, integration, and end-to-end — in an AI-assisted development context. 

• Understanding of SaaS platform architecture and governance, including integration patterns, identity and access management, data flows, vendor release management, security controls, and product lifecycle ownership. 

Product & Design Fluency — Required 

• Proven ability to frame and run hypothesis-driven product development at pace. 

• Experience defining product strategy and roadmap in ambiguous, fast-moving environments. 

• Ability to articulate a compelling product vision that aligns stakeholders, guides roadmap tradeoffs, and creates a clear narrative for how technology enables differentiated business and employee outcomes. 

• Strong instincts for user experience — able to evaluate AI-generated designs critically and advocate for the user when speed creates pressure to cut corners. 

• Ability to define and track outcome-based metrics that distinguish real progress from activity. 

Experience 

• Extensive experience spanning product engineering, software engineering, solution architecture, platform engineering, product management, or technology leadership. 

• People leadership experience, including experience leading leaders or scaling multi-team organizations. 

• Experience operating in a senior leadership role that reports directly to a functional technology head or equivalent executive stakeholder. 

• A track record of portfolio-level ownership — translating strategy into roadmaps, budgets, team structures, delivery outcomes, and measurable business impact. 

• Experience leading organizations through adoption of AI-assisted development workflows, including governance, enablement, quality control, and productivity measurement. 

• Experience managing budgets, headcount plans, vendor/resource strategy, and investment tradeoffs in partnership with finance and senior leadership. 

• Bachelor's degree in a relevant field or equivalent practical experience; advanced degree or equivalent executive leadership experience is a plus.