AVP, Data Governance

About the role

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.

Responsibilities

• Data Quality Lifecycle Ownership:
  • 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 used for advanced analytics meets fit-for-purpose quality standards.
• Data Protection, Privacy & Masking:
  • Own the design and execution of data protection controls, translating Legal and Compliance requirements into actionable governance standards, controls, and enforcement mechanisms; Define and enforce governance expectations for sensitive data usage and handling, ensuring alignment with Legal, Compliance, and Information Security requirements; Ensure masking and privacy controls are implemented consistently and monitored 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.
• Master Data Management (MDM):
  • 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.
• Stewardship Support, Training & Domain Enablement:
  • Partner data governance leadership and team members, with specific ownership for: Data quality–related training content and domain-specific enablement (e.g., privacy handling, MDM practices); DQ practices, standards, and execution guidance for stewards; Contribute to and continuously enhance domain-specific training content and materials within the broader stewardship curriculum, ensuring alignment with governance standards, tools, and real-world use cases; Develop and refine repeatable processes, playbooks, and guidance to enable stewards to effectively identify, triage, and resolve data quality issues; Reinforce stewardship accountability by ensuring domain-specific expectations (e.g., data quality, privacy, MDM and data readiness expectations for analytics and AI) are clear, actionable, and consistently applied; Support adoption of privacy, data protection, and MDM-related expectations through clear, practical guidance for data stewards; Support translation of governance intent into practical, steward-facing execution guidance.
• Forums, Facilitation & Decision Flow:
  • Own the purpose, decisions, and outcomes of DQ and DGC working groups, including shaping issues, driving resolution, and preparing escalation into the Data Governance Council (DGC), ensuring implications for analytics and AI use cases are clearly surfaced and factored into decisions; Personally facilitate sessions when: Issues are complex or cross-domain; Remediation is stalled; Alignment or accountability is unclear; Adapt forum structure and cadence based on effectiveness and outcomes; Ensure discussions result in clear decisions, actions, owners, and timelines.
• Initiative Delivery, Change & Risk Management:
  • Lead execution of data quality, privacy, and MDM initiatives within SVP-approved scope and decision boundaries, ensuring alignment to governance intent and outcomes; Identify, manage, and escalate delivery, alignment, and adoption risks requiring reprioritization or senior intervention; Stay hands-on as needed to maintain momentum, including drafting materials, framing logic, capturing decisions, and driving follow-through to closure; Own the business definition and delivery of assigned governance tool capabilities and roadmaps, including requirements definition, prioritization, feature sequencing, and leading and executing user acceptance testing (UAT) to ensure delivered capabilities meet governance intent and adoption needs; Partner closely with the Program Specialist to deliver assigned governance initiatives, while remaining directly accountable for execution quality, outcomes, and adoption; Lead initiative-specific communications and change execution, including message intent, readiness, reinforcement, and active management of resistance informed by adoption signals; Own status reporting and KPIs across assigned initiatives, translating delivery, adoption, and risk signals into executive-ready insights; Ensure metrics are actively tracked, interpreted, and used to drive decisions and prioritization, not just reporting; Ensure data catalog coverage, quality, and freshness are treated as governance outcomes and reviewed alongside delivery and adoption indicators to inform prioritization and escalation; Identify, own, and actively manage data quality, privacy, MDM, and governance risks, ensuring material risks are captured and maintained in the Data Team risk register with clear ownership, impact, and mitigation; Incorporate analytics and AI considerations into initiative planning, delivery, risk management, and change execution, ensuring governance risks related to data quality, transparency, explainability, and misuse are identified, tracked, and actively managed.

Operating Expectations

• Operates effectively in a startup-like, evolving environment, adapting priorities, processes, and execution as new information emerges while maintaining forward momentum; Demonstrates a builder mindset by creating clarity, structure, and momentum where they do not yet exist; Applies a trust-but-verify approach by validating assumptions, testing logic, and confirming decision boundaries before scaling solutions; Surfaces misalignment early and recalibrates based on new information or leadership feedback; Leads through facilitation, presence, and follow-through rather than hierarchy, flexing comfortably between strategy, facilitation, and hands-on execution to drive clarity, momentum, and outcomes; Demonstrates strong coachability by actively seeking feedback, aligning quickly to SVP direction, and treating evolving expectations as a natural part of a build environment; Encourages open discussion and constructive challenge early, then drives aligned execution once decisions are made to ensure timely, consistent follow-through; Technically fluent and comfortable self-learning governance tools and capabilities, translating complex functionality into clear, business-ready presentations and learning content for diverse audiences; Demonstrates applied literacy in analytics and AI concepts, with the ability to clearly explain how AI initiatives depend on strong data governance foundations and to translate governance requirements into practical expectations for both business and technical audiences.