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Hagerty is a company built by drivers for drivers. We put our members at the center of everything we do and are dedicated to making it easier and more enjoyable for enthusiasts to drive and celebrate the machines they love. We’re proud to be the world’s largest insurer of collectible and enthusiast vehicles and are home to the Hagerty Drivers Club, the world’s largest car club. Our Marketplace business presents live and digital sales across the U.S. and Europe, we host a number of driving events and concours, and our award-winning automotive journalists produce the most popular car magazine globally, alongside internationally awarded videos. We’re committed to Never Stop Driving. Ready to get in the driver’s seat? Join us!

As the Group Technical Product Manager for Enterprise Data, Analytics, and AI/ML, you will lead a portfolio spanning Enterprise Data, Analytics, and AI/ML — owning strategy and delivery across the full data value chain.

You will lead a small team of Technical Product Managers accountable for enterprise data platforms, ML models, generative AI applications, data pipelines, and business intelligence. This is a player-coach role: you willown cross-cutting technical strategy and escalation decisions, while also serving as a Technical Product Manager for one or more value streams.You’llbe responsible for staying close enough to delivery to resolve the hard calls when your team needs you.

You will sit at the intersection of product strategy, data platform leadership, and people management — partnering with Engineering, Data Science, Design, and senior business stakeholders to ensure Hagerty's enterprise data and AI capabilities evolve faster than the market demands.

What you’ll do

Portfolio Strategy & Vision

Own the strategy and roadmap across Enterprise Data, Analytics, and AI/ML — enterprise data platforms, data pipelines, ML models, generative AI, and BI — ensuring alignment with company objectives.

Classify and sequence investment across feature work, adoption, platform scaling, and new-capability expansion to maximize portfolio ROI.

Identify and prioritize cross-product dependencies, platform investment priorities, and build-vs-buy decisions across all three domains.

Partner with senior stakeholders to shape long-term platform vision, balancing innovation with foundational reliability and data quality.

Translate enterprise data strategy into actionable product priorities for your TPMs.

Team Leadership & Development

Lead, coach, and develop a small team of TPMs covering Enterprise Data, Analytics, and AI/ML — fostering high performance and a strong ownership culture.

Deliberately delegate high-visibility, high-complexity initiatives as stretch assignments; build systems, frameworks, and review cadences that enable TPMs to operate independently.

Provide ongoing performance feedback, career development support, and clear expectations for product excellence.

Model player-coach behavior: engaged enough in delivery to remove blockers, strategic enough to keep the team focused on what matters.

Calibrate workload and capacity across your TPMs, ensuring each has a clear and manageable scope.

Execution Oversight & Delivery

Ensure consistent, high-quality execution across enterprise data platform delivery, ML model productionization, AI feature rollout, and BI development.

Guide teams in prioritization and tradeoff decisions across new capabilities, technical debt, scalability, and compliance obligations.

Stay hands-on where it counts — joining critical ceremonies, unblocking decisions, and owning the hard prioritization calls that require Group-level authority.

Hold TPM plans to Hagerty's product plan review standard: measured outcomes tied to enterprise levers, an economically prioritized flow with a named constraint, and a probabilistic delivery forecast.

Technical & Platform Leadership

Produce and maintain the portfolio-level roadmap architecture — mission, product/system, and technology layers — aggregating Enterprise Data, Analytics, and AI/ML domain roadmaps into one coherent view with figures of merit and technology-readiness levels per initiative.

Apply evolution mapping across the enterprise data and AI stack to justify build, buy, and outsource decisions, and assign each initiative to a horizon with an explicit investment split

Group Technical Product Manager at Hagerty