Senior Manager of Enterprise Data Architecture

Hagerty

1d•Remote

About The Position

Hagerty is seeking a Senior Manager of Enterprise Data Architecture to lead a team of data architects and own enterprise-scale data management practices. The role sets architectural direction for master data, analytic data domains, and data assets that support AI solutions, while partnering with Data Engineering to deliver production systems. The successful candidate brings strong data architecture fundamentals and people leadership, and balances immediate business value with long-term goals. The position is open to U.S. remote work. Team members living within 20 miles of Hagerty’s Traverse City headquarters follow a hybrid schedule and work from the office three days per week. The role may require travel for quarterly events. Familiarity with public-company requirements, including Sarbanes-Oxley and applicable regulations, is noted; for SOX-compliant roles, responsibilities may include designing, executing, and documenting assigned internal controls and attesting to the completeness, accuracy, and compliance of financial reporting data, where applicable.

Requirements

Bachelor's degree in computer science, information systems, or a related field, or equivalent experience.
At least 5 years of data architecture experience, including at least 2 years managing people.
Strong foundation in data architecture fundamentals and the DAMA-DMBOK framework.
Deep experience in master data management and experience partnering with engineering teams to operationalize systems of record.
Experience guiding development of data domains, data products, and data assets that support analytics and AI.
Entrepreneurial and pragmatic judgment to balance delivering near-term value with investing in long-term foundations.
Strong attention to detail and high standards for the accuracy of data models, definitions, and documentation.
Clear communication skills, ability to build a compelling narrative, and ability to influence cross-functional decisions.
Experience managing vendors and third-party providers, including responsibility for deliverables and budgets.
Ability to coach a team through ambiguity.
Familiarity with public-company requirements, including Sarbanes-Oxley and applicable regulations, where relevant.

Nice To Haves

Experience with Snowflake or other cloud data platforms.
Experience with dbt.
Experience with data modeling tools such as SqlDBM, erwin, or ER/Studio.
Experience with data catalog and governance tools such as Atlan.
Proficiency in SQL; Python is a bonus.
Experience working with third-party data validation or enrichment providers.
Experience applying AI-assisted tooling to architecture and modeling work.

Responsibilities

Lead and develop a team of seven data architects, providing direction, context, and transparent career paths.
Own master data management practices for members, agents, and vehicles, and partner with Solution Architecture and Engineering on delivery plans and quality-certified systems of record.
Define standards for attribute validation and certification through third-party data providers or human-in-the-loop verification.
Serve on the data governance team, providing architectural oversight and policy recommendations.
Work with business and technical leaders to develop and refine practical data management policies.
Guide the design of data domains, data and analytic products, and data assets for AI solutions, with the goal of making analytic data domains available to every core business group.
Establish clear partnership boundaries with Data Engineering, including ownership, handoffs, and accountability.
Improve team practices, including adopting AI-assisted tools for modeling, documentation, and design review.
Review and coach the team on design quality, stepping in directly when needed.
Maintain an architectural vision aligned with Hagerty's enterprise architecture to guide maturation of the Enterprise Data domain.
For SOX-compliant roles, design, execute, and document assigned internal controls and attest to the completeness, accuracy, and compliance of financial reporting data, where applicable.