Moorepay is building a Platform for Workforce Operations, where Payroll, HR and Workforce Management run on one data model, one workflow engine and one experience. We build it with an AI-first product and engineering operating model, and that model only works if the systems behind it work: the context our AI agents read, the tools our teams use, the data that tells us whether we are succeeding.
We're hiring a Senior Product Operations Manager to own that machinery for the Product function. This is a technical enablement role. You will build automations, run the Product Brain, design reporting pipelines and help every product person get more out of AI. Tooling administration is part of the job, and it is the smallest part.
The Opportunity:
Product strategy is set at the top and delivered by small, autonomous, cross-functional AI Pods (Engineering, QA, UX and Product), each accountable for measurable outcomes. Work moves through a structured twelve-step process, from strategy and OKRs to workstream briefs, outcome definitions, design intents and AI-ready specifications, then through build, validation and a learning loop.
That process runs on structured context. The Product Brain, our retrieval layer over the platform, domain and workstream context documents, is what our AI tooling reads before it helps anyone specify or build. If that context is stale, inconsistent or hard to find, the quality of everything downstream drops. The same is true of our tooling, our data and our ways of working.
As Senior Product Operations Manager, you own the operational backbone of the Product function. You make sure the Product Brain is accurate and well governed, the tools are joined up and automated, the reporting tells us the truth about delivery and outcomes, and every product person is confident using AI in their daily work. You work alongside our Engineering Process Lead, who owns engineering tooling and pipelines; your remit is the Product side of the operating model.
Within your first six months you will lead a time-boxed transition from a designed operating model to one that runs every day. You will run it as a phased plan, with exit criteria agreed with the CPO and oversight from a small steering group the CPO chairs.
Key Responsibilities:
- Mobilise and baseline - Audit the Product Brain and toolchain, baseline the metrics, and agree the plan and exit criteria.
- Pilot - Prove the controls, metrics and automations with a small number of pods.
- Scale and embed - Roll them out to every pod and to the go-to-market handover, and make them business as usual with clear ownership, cadence and training.
- Compliance controls and the launch-readiness framework will continue into the second half of the year, because they depend on other functions and on real launches to prove them.
- Alongside the plan you will run the change work: building capability across the function, and finding and working through resistance early.
- Own the Product Brain - Run our context RAG as a product in its own right: ingestion, structure, source precedence, freshness and retrieval quality. Make sure that what our AI tooling reads is current, accurate and traceable to an owner, with an audit trail of sources, changes and approvals so we can show what context our AI used and who signed it off.
- Govern the context hierarchy - Keep the platform, capability, domain and workstream context documents healthy, and own the standard templates for those documents and for PRPs. Track ownership and review dates, flag stale or conflicting content, and make sure updates from the learning loop actually land.
- Embed operating controls - Work with Risk and Compliance to build controls into the twelve-step process: traceability from regulatory change to the context and specifications it affects, data-residency rules for what AI context can hold and where it can be processed, and audit evidence produced as the work happens. You should be able to answer what our AI read before a statutory calculation changed.
- Lead AI adoption across Product - Be the AI capability owner for the Product function. Coach product managers, product owners and UX colleagues to use AI well, build playbooks and prompt patterns, and measure whether adoption is changing how people work.
- Build and maintain our skills and agents - Own the register of Moorepay AI skills and agents used by Product. Design, test and improve them with the people who use them, and retire the ones that no longer earn their place.
- Automate the friction out of our processes - Find the manual, repetitive steps in how Product works and remove them. Connect our tools (Aha!, Azure DevOps, Miro, Copilot and Claude) so that information moves without someone retyping it.
- Own the Product metrics framework - Define the metrics that show whether the operating model is working, across five layers: outcome, flow, quality, AI effectiveness and cost. Write the definition and data lineage for each, starting with the measures we rely on today (PRP quality and delivery scores, Kanban flow and OKR progress) and adding product usage from product analytics. Build the pipelines and dashboards that report them, and produce a quarterly board page on the operating model.
- Run the operating rhythm - Chair the weekly Product productivity review and turn its data into decisions for the CPO and Principal PMs. [To confirm: the other forums this role chairs, and the forums it prepares for.]
- Own the launch-readiness framework - Define launch tiers, the readiness checklist (operational, support, legal, commercial and customer communication), early-access design and the post-launch review. Pods and go-to-market teams own each release and each checklist dimension within it. You own the framework and automate the outputs and evidence it needs, such as release notes, marketing material and how-to guides.
- Improve the operating model - Watch how the twelve-step process runs in practice, spot where it slows down or breaks, and propose changes with evidence. Keep process documentation and onboarding materials current as the model evolves.
- Run the Product toolchain - Own the configuration, integration and governance of Product tools, including licences, access and cost. Keep this lean and automated so it stays a small part of the job.
- Co-own the seam between specification and code - Agree with the Engineering Process Lead a shared definition of the PRP handoff and one joined-up view of flow from workstream brief to release, with each side owning its own metrics. Product-side skills and agents sit with you; engineering agents and the PR-to-release pipeline sit with Engineering.
Your Authority:
- You decide - Standards for the Product toolchain, the rules for what goes into the Product Brain and which sources take precedence, template standards and metric definitions.
- You can veto - Content entering the Product Brain that fails governance, and a launch that fails the readiness gate.
- You recommend - Changes to the operating model, changes to roles, and tooling spend above budget, with the CPO deciding.