AI Engineering Lead, Product Analytics

Summary:

Product Analytics is building self-service tools and operating AI agentsthat influence product development; agents thatmonitorproduct health surface anomalies analyze user behavior and produce theinsightsproduct leaders rely on. As more analysts build the work needssomeone to scale agentic solutions and ownthe shared infrastructure underneathit. We are seeking an AI EngineeringLeadto own that layer across a team ofroughly 35analysts supporting 60 products. You will build the shared repositories standards context and evaluation tooling our analysts depend on and you will define what production means for the teams AI work. You will also build agents yourself often expanding what others have prototyped into something the whole team can use. This is a hands-on role; you build and you keep our builders moving faster.

This is an ongoing leadership role that evolves as the field does reporting directly to the VP Product Analytics. The role reaches across TR. You will advocate for the data and tooling the team needs push to get the right sources into the data lake work with engineering and TRs central Data and Analytics team and connect with AI leaders in other product groups so our work compounds with theirs.Within 12 months we expect agents owning whole analytics workstreams and this role builds the foundation that gets us there.

About the Role:

As AI Engineering Lead Product Analytics you willbe responsible for:

• Own the Shared Infrastructure:Build andmaintainthe shared assets our analysts build on: the teams Git repositories reusable components context and data-access standards and a registry of what exists and who owns it. Take what individual builders make locally and generalize it so the whole team can use it. Build this as self-service so analysts move forward by using the tooling not by waiting on you.

• Build Agents:Build production AI agents yourselffrequentlyby picking up a tool another analyst prototyped and extending it into something more capable and broadly useful. Stay close enough to the build to keep your judgment about the tooling sharp.

• Own Evaluations and the Definition of Done:Define what production means for the teams AI work and own the evaluation standard that holds it there. Build the tooling that lets analysts run evalsthemselves andbring the teams evaluation practice up over time.

• Close Pipeline Gaps:Find the breaks between collecting the right data and shipping the self-service AI tooling product managers use to understand user behavior in our products. Diagnose where data context or infrastructure is missing drive the work to close those gaps and advocate to get the right sources into the data lake.

• Set the Build Standards:Own how the team creates and manages its build artifacts: repository conventions context files documentation that makes agents reliable. Keep these changes cheap and fast to make so the standards speed builders up. Propose with conviction which workstreams should move fully to AI first and sequence them so early wins build credibility.

• Make Builders Better:Bring analysts along by teaching the infrastructure they use; the person who sets the eval standard and the repository conventions is the one who shows people how to work with them. Keep the upskilling tied to real deliverables and to tooling people already touch so the practice sticks.

• Governance and Compliance:Navigate TRs AI governance landscape on the teams behalf. Help analysts buildto TR standards support compliance where agents touch sensitive data and decisions and keep governance workable so it does not block shipping.

• Scale Adoption Across the Team:Make the teams tools findable and usable by someone who has no direct relationship with whoever built them. Keep the registry current manage how tools move from prototype to shared and depended-on and catch drift before it reaches stakeholders.

• Interface Outward:Represent the team in TR-wide AI conversations connect with AI leaders in other product groups and keep the link to TRs AI transformation program active. Manage the cross-team dependencies the work runs on including data lake access and platform infrastructure.

• Keep Production Agents Healthy:Establishhow the team watches its own agents once they run in production so breakage and quality drift get caught early. Give every production tool a clear owner and a monitored definition of done.

About You:

This role suits someone who builds and who has run programs that scale across a team. You are a strong engineer who works alongside other builders shaping infrastructure with them so they trust it and use it and you have driven enough change to know how adoptionactually happens. You are a fit for the role of AI Engineering Lead if your background includes:

• Demonstrated personal investment in AI: you actively track developments experiment with new tools and build things on your own initiative. Depth of curiosity and momentum carry the weight here.

• Roughly 2 years of serious hands-on building with modern AI tooling with work you can point to. You are fluent across AI assistants coding in an AI development environment and the patterns of agent design fluent enough to build production-grade tools and the shared infrastructure other builders rely on. Experience taking someone elses prototype and generalizing it into reusable infrastructure is a strong signal and self-directed projects count as much as anything done on the job.

• A workingknowledgeof how to evaluate AI systems: defining success criteria building evals and using them to decide what is ready for production. You can set this standard for others and improve it over time.

• Substantial experience building structure and programs that scale a capability across a whole team: standards processes cadences and documentation. You have done some of this before; we expect the rest to grow on the job.

• Strong eye for detail and the discipline to specify what success looks like before building and verify it continuously after.

• The judgment to know when to push the team toward AI and when a workflow is not ready for it and the credibility to articulate that vision to analysts and product leaders alike.

• 4 years driving change across teams and with senior stakeholders witha track recordof getting people to adopt new ways of working. Much of this roles success is a change-management problem: the infrastructurehas tobe good and peoplehave tochoose to use it.

• 5 years in analytics or a closely related data discipline with working command of a modern stack (e.g. Snowflake SQL Python BI tools such as Power BI or TableauStreamlit) sufficient to lead technical work and judge tool quality.

• Awareness of AI governance and compliance considerations.

• Project tracking and program coordination experience (e.g. Linear Azure DevOps SharePoint) is an asset.

• Experience in or alongside product teams and SaaS experience are assets.

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