AVP, Data Governance

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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.

Core Responsibilities

1. 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.

2. 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.

3. Master Data Management (MDM)

• 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.

4. 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 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.

5. 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.

6. Complex Data Quality Issue Facilitation

• When designated, serve as Complex Data Quality Issue Facilitator, responsible for:
  • driving cross‑department coordination across impacted stewards and teams
  • directing the focus of coordinated remediation efforts
  • ensuring progress, accountability, and follow‑through
  • coordinating regular updates with impacted Data Owners
• This responsibility is assigned case‑by‑case based on issue complexity and expertise needs and may also be fulfilled by other qualified roles.

7. 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.

Experience & Background

Required

• Bachelor’s degree in related discipline.
• Experience leading data quality, data protection/privacy, or MDM capabilities in complex environments, with a proven ability to deliver outcomes in ambiguous, cross‑functional environments.
• Demonstrated comfort balancing leadership accountability with hands‑on execution, driving complex initiatives across business, data, and technology stakeholders with strong execution discipline.
• Proven ability to operate independently while remaining aligned to senior leadership intent and decision boundaries, applying sound judgment, validating assumptions, and surfacing risks or misalignment early.
• Strong written and verbal communication skills, including the ability to produce executive‑ready presentations, training materials, and enablement artifacts.
• Demonstrated openness to feedback, continuous improvement, and recalibration based on evolving priorities and leadership direction.
• Strong organizational skills and follow‑through, with a track record of driving decisions to closure.
• Proficiency with core productivity and analysis tools (e.g., PowerPoint, spreadsheets) to support planning, tracking, and interpretation of governance outcomes.
• Demonstrated ability to adapt prior experience, frameworks, and best practices to new organizational contexts—including regulated insurance environments and evolving operating models—rather than applying previous solutions by default.
• Working knowledge of analytics and AI concepts, including how data quality, lineage, metadata, and governance controls impact AI reliability, transparency, and business risk.

Preferred

• Experience in P&C insurance or regulated industries a plus.
• Familiarity with data quality tools, masking technologies, or MDM platforms.
• Experience building or materially evolving governance programs rather than solely operating mature ones.
• Experience supporting or leading training programs or change initiatives in data, governance, or technology contexts.
• Familiarity with dashboarding and visualization tools (e.g., Power BI or similar) to support interpretation and communication of metrics.

For individuals assigned or hired to work in the location(s) indicated below, the base salary range is provided. Range is as of the time of posting. Position is incentive eligible.

123,400.00 - 191,628.66

• Total individual compensation (base salary, short & long-term incentives) offered will take into account a number of factors including but not limited to geographic location, scope & responsibilities of the role, qualifications, talent availability & specialization as well as business needs. The above pay range may be modified in the future.

• Arch is committed to helping employees succeed through our comprehensive benefits package that includes multiple medical plans plus dental, vision and prescription drug coverage; a competitive 401k with generous matching; PTO beginning at 20 days per year; up to 12 paid company holidays per year plus 2 paid days of Volunteer Time Offer; basic Life and AD&D Insurance as well as Short and Long-Term Disability; Paid Parental Leave of up to 10 weeks; Student Loan Assistance and Tuition Reimbursement, Backup Child and Elder Care; and more. Click here to learn more on available benefits.

   

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