AVP, Data Governance

With a company culture rooted in collaboration, expertise and innovation, we aim to promote progress and inspire our clients, employees, investors and communities to achieve their greatest potential. Our work is the catalyst that helps others achieve their goals. In short, We Enable Possibility.

The AVP, Data Governance - Data Quality, Privacy & MDM is responsible for owning and advancing Arch Insurance North America's data quality lifecycle, data protection controls (including masking and privacy), and master data management (MDM) use cases, while supporting execution of broader Data Governance initiatives as needed. Reporting to the SVP, Data Governance, this role is accountable for delivery, adoption, and outcomes across these domains, acting as both a decision facilitator and active builder within a dynamic, evolving data governance environment.

This role is intentionally designed for individuals who are comfortable operating in ambiguity, validating logic and assumptions, and stepping into execution when clarity, structure, or momentum is required. Success is measured not just by framework design, but by sustained remediation outcomes, risk reduction, and business adoption.

The AVP is expected to translate governance intent into practical, executable solutions across business, data, and technology stakeholders, while remaining aligned to SVP-defined priorities, scope, and decision boundaries.

This role plays a critical part in enabling responsible and scalable analytics and AI by ensuring that data quality, privacy controls, and master data are trustworthy, governed, and fit for use and AI-driven decisioning and automation. The AVP is expected to understand how AI and advanced analytics depend on high-quality data, clear ownership, and strong metadata, and to incorporate these considerations into stewardship models, standards, enablement, and change execution.

This role operates in a highly dynamic environment where Data Governance capabilities, processes, and operating models are actively being built and refined, requiring comfort with ambiguity, iteration, and continuous improvement. Responsibilities may evolve over time to reflect changes in business, analytics, AI, or governance priorities, operating models, or initiative needs, while remaining aligned to this role's core mandate and accountability.

• Own the design, execution, and continuous improvement of the enterprise Data Quality (DQ) lifecycle, including:
  Issue intake, triage, and prioritization
• Root cause analysis and remediation coordination
• Ongoing monitoring, controls, and sustainability
• Define and enforce standards for data quality measurement and remediation expectations across domains.
• Ensure data quality issues are explicitly tracked, assigned, and driven to closure, with clear ownership and accountability.
• Partner with Data Owners, Stewards, and technology teams to ensure business-aligned remediation outcomes, not just technical fixes.
• Incorporate analytics and AI dependencies into DQ expectations, ensuring data usedfor advanced analyticsmeetfit‑for‑purposequalitystandards .

• Ownthedesignandexecutionofdataprotectioncontrols, translating
  
  Legaland
  
  Compliance requirements into actionable governance standards, controls, and enforcement mechanisms
• Defineandenforcegovernanceexpectationsforsensitivedatausageandhandling, ensuring alignment with
  
  Legal,Compliance, and Information Security requirements
• Ensuremaskingandprivacycontrolsareimplemented consistentlyandmonitored for effectiveness.
• Identify and support remediation of risks related to data misuse, exposure, or regulatory non‑compliance through appropriate data protection controls, including use in analytics and AI‑enabled processes, escalating where enterprise risk is present
• Partner with Data Stewards to ensure data classification, handling, and protection expectations are consistently applied and sustained across domains.
• Translate privacy and protection requirements into clear, business-understandable expectations and execution steps.

• Own business‑aligned definition, prioritization, and delivery of MDM use cases (e.g., reference data, key entities such as Account or Insured).
• Define and govern data conformance standards, reference value structures, and governance controls, in partnership with Data Owners, Data Stewards, and SMEs, to ensure consistency and reuse.
• Partner with business, data, and technology stakeholders to ensure MDM solutions deliver:
• Consistent definitions
• Controlled data creation and updates
• Improved downstream usability, reporting, and reliable use in analytics and AI applications
• Ensure MDM initiatives are practical, adoptable, and tied to real business outcomes, not purely technical implementations.

• Partner data governance leadership and team memb