Master and Reference Data Management Lead

Everest•Warren Township, NJ

12d•$156,000 - $180,000•Hybrid

About The Position

Everest is seeking a Master and Reference Data Management Lead to join our Data & Analytics function in Warren, New Jersey, and shape the development of master and reference data capabilities across our global Insurance business. You will help build trusted, governed sources for the core entities, hierarchies and classifications that support underwriting, operations, finance, analytics and regulatory reporting. This is a hands-on leadership opportunity with influence across business and technology teams. A central focus will be modernizing our master and reference data management platform capabilities. You will lead business and data requirements, assess existing processes, and partner with Technology and Architecture on solution evaluation, selection, implementation and adoption. You will establish clear business ownership, stewardship and controls, and ensure trusted master and reference data supports Everest’s enterprise data platform and data product ecosystem. Success will combine a practical modernization roadmap with greater adoption, better data quality and less manual maintenance.

Requirements

8–10 years of experience in data management, data governance or related disciplines, including substantial practical experience in master and/or reference data management.
Experience designing or implementing master or reference data management capabilities, operating models and governance frameworks in a complex organization.
Experience helping design, select, implement or modernize enterprise master and reference data technology capabilities, translating business and governance needs into platform requirements.
A sound understanding of modern master and reference data technologies, data quality, metadata, lineage, reconciliation and integration patterns.
The ability to turn business problems into clear data requirements, practical governance controls and implementable solutions.
Strong stakeholder management skills and the confidence to work with business leaders, data owners and stewards, architecture, engineering and technology teams.
A clear understanding of business ownership of data, governance accountability and Technology’s responsibility for engineering and platform delivery.
Experience with cloud-based enterprise data platforms and modern data architectures, alongside the judgment to balance strategic direction with pragmatic delivery.

Nice To Haves

Insurance, or broader financial services experience is highly desirable, particularly in regulated data environments.

Responsibilities

Define and drive the Master Data Management and Reference Data Management strategy, target state, roadmap, operating model and standards for the Insurance business including the evolution or replacement of existing capabilities and the potential role of artificial intelligence-driven entity resolution.
Lead business and data requirements for platform modernization, partnering with Technology and Architecture to assess solutions, shape the target architecture and support implementation, migration and adoption.
Establish clear ownership boundaries, stewardship, change controls and approval workflows across Data Governance, Technology, platform teams and business teams, reducing inappropriate reliance on manual operational intervention.
Establish trusted records, hierarchies and governed taxonomies for priority entities and reference sets, and ensure they are consistently integrated across operational systems, the enterprise data platform and enterprise data products.
Define and embed control standards and accountability for master and reference data, including validation, data quality, lineage, reconciliation, audit, regulatory and risk requirements.
Oversee day-to-day master and reference data operations, ensuring service performance, data issues, exceptions, change requests, risks and dependencies are actively managed and transparently reported.
Increase automation and governed self-service, addressing recurring issues at source and reducing manual maintenance and dependency on individual knowledge.
Define meaningful measures for data quality, adoption, exceptions and change activity, and use them to drive continuous improvement and good practice across the organization.