Group Technical Product Manager

Hagerty

1d•Remote

About The Position

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 will own cross-cutting technical strategy and escalation decisions, while also serving as a Technical Product Manager for one or more value streams. You’ll be 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.

Requirements

8+ years of product management experience, with significant experience in enterprise data, analytics, ML, or AI-focused roles.
3–5+ years of people management experience, including coaching and developing TPMs or similar roles.
Proven track record leading enterprise data and AI product portfolios — from raw data ingestion through ML models, generative AI features, and BI consumption.
Hands-on familiarity with the full data product lifecycle: enterprise data platforms, pipelines, model development, feature engineering, deployment, monitoring, and iteration.
Strong understanding of LLM application patterns and the product challenges of building reliable, safe AI-driven experiences.
Working fluency in outcome-driven prioritization (Jobs to Be Done, Kano), economic sequencing (cost of delay, WSJF), capability roadmapping (layered roadmaps, Wardley mapping), and probabilistic delivery forecasting — sufficient to coach TPMs against Hagerty's product plan review standard.
Demonstrated ability to operate at both strategic and execution levels.
Strong technical acumen with the ability to engage meaningfully with data engineers, data scientists, ML engineers, and software engineers.
Excellent communication and stakeholder management skills, with the ability to influence at senior and executive levels.
Insurance or financial services industry experience is a plus.
Familiarity with public company requirements, including Sarbanes Oxley and key regulations, if applicable. For SOX compliant roles, responsible for designing, executing, and documenting internal controls where they have been identified as owners to prevent errors in financial reporting, processes, and business operations. Including attestation to the completeness, accuracy, and compliance of all financial reporting data, where applicable.

Nice To Haves

Experience with enterprise data platforms or cloud data warehouses (e.g., Snowflake, Databricks) and ML orchestration tools.
Experience with agentic AI patterns, frameworks, or production agentic experiences.
Familiarity with model risk management frameworks or AI governance practices in regulated industries.
Exposure to Azure AI services, Azure ML, or comparable cloud-native AI/ML platforms.
Familiarity with Azure DevOps or similar agile delivery platforms.
Experience in compliance-driven product development (e.g., SOX, state insurance regulations).
Experience leading platform modernization or enterprise data infrastructure transformation initiatives.

Responsibilities

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.
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.
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.
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 so near-term delivery does not starve platform work.
Stay current on AI/ML advances and the evolving regulatory landscape for AI in insurance.
Ensure enterprise data products meet data governance, model risk management, and compliance requirements.
Act as the senior product voice for Enterprise Data, Analytics, and AI/ML across Engineering, Data Science, Operations, and business leadership.
Identify what is blocking portfolio outcomes — including platform capacity, governance, or data engineering priorities outside your direct line — and influence leadership to resolve it.
Communicate portfolio strategy, delivery progress, risks, and tradeoffs clearly at all organizational levels — from sprint review to executive briefing.
Establish and evolve product management processes, backlog standards, and delivery practices across Enterprise Data, Analytics, and AI/ML.
Drive consistency in discovery, definition, and delivery execution across all three domains.
Implement mechanisms to track portfolio health, manage risk, and continuously improve team effectiveness.
Define and monitor KPIs across enterprise data products and AI capabilities, ensuring alignment with business outcomes.
Champion measurable impact: enterprise data platform reliability, model adoption, BI utilization, and time-to-insight.
Ensure your team maintains deep understanding of internal and external customer needs, enterprise data workflows, and business outcomes.
Oversee alignment with regulatory and compliance requirements, including model risk management, SOX, data privacy, and AI governance standards.
Stay informed on industry trends in insurance analytics and AI-driven underwriting.