Head of Product Engineering, HR Total Rewards - Director

The Role

TheHead of Product Engineering, HR Total Rewardsis a senior product and engineering leaderreporting directly to theHead of HR Technology,accountable fordefining the product vision andbuilding, 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 remaindeeply grounded in hands-on product and engineering judgment — including theexpectation to stay actively involved in coding, code review, prototyping,AI-assisted development, internal developer platforms,vendor platforms,and modern engineering practices — while expanding accountability toproduct 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 towrite and review code when needed, build prototypes, validate AI-generated output,understand product tradeoffs,assesstechnical risk,influencearchitectural direction, andimprovedelivery 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.

Yousetthe standard for how theorganizationworks, not just whatitships. You model the durable skills —Observant, Curious,Determined— andinstitutionalizethemthrough 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,deliveryhealth, 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,validatehypotheses, and make tradeoffs across user value, technical feasibility, risk, cost, and speed.

• Identifypatterns 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

• Establishorganization-widearchitecturalprinciples, engineeringguardrails, security expectations, and delivery standardsthat enablemultiple teamsto movequicklywithout accumulating unsustainable technical ordesigndebt.

• 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.

• Drivebuild-vs-configure-vs-orchestrate decisionsat portfolio level,leveragingthe 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 feedbackquickly, andestablish the metrics that matter before building begins.

• Challenge assumptions at every stage — including your own.

🛠️Lead Technical Validation

• Remainhands-onenough to write code, review pull requests,inspect critical technical decisions,validatehigh-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.

• Continuouslyevolvethe 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 wehire forfirst.They cannot be taught by a framework or acquired through a certification.

OrganizationalLeadership — 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.

• Comfortoperatingas an executive-levelplayer-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 thoserequiringbehavioral 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, notreplaceteam ownership.

• Ability tocritically review andvalidateAI-generated codeacross multiple languages —identifyingcorrectness issues, security vulnerabilities, and architectural anti-patterns.

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

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

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

• Ability to define and enforcetesting 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 runhypothesis-driven product developmentat pace.

• Experience definingproduct strategy and roadmapin 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 foruser experience— able to evaluate AI-generated designs critically and advocate for the user when speed creates pressure to cut corners.

• Ability to define and trackoutcome-based metricsthat distinguish real progress from activity.

Experience

• Extensive experience spanningproduct engineering,software engineering,solution architecture, platform engineering,product management,ortechnology leadership.

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

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

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

• Experience leading organizations through adoption ofAI-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 fieldor equivalent practical experience; advanced degree or equivalent executive leadership experience is a plus.