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TheAVP, Data Governance - Stewardship, Metadata & Enablementis responsible forowning and advancing Arch Insurance North America's data stewardship model, business metadata foundations, and governance enablement capabilities. Reporting to the SVP, Data Governance, this role is accountable for delivery, adoption, and outcomes; 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,validatinglogic and assumptions, and stepping into execution when momentum, clarity, or credibility requires it. Success is measured not just by strategy or structure, but by adoption, outcomes, and sustained execution across business and data stakeholders.

The AVP, Data Governance is a self-starter who is expected toidentifywhat must be done next, propose an approach, and move execution forward with minimal prompting-while staying closely aligned to SVP intent and decision boundaries.

This role plays a critical role in enabling responsible, transparent, and scalable use of analytics and AI by ensuring that stewardship, metadata, lineage, and governance controls are fit for 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 roleoperatesin 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, whileremainingaligned to this role's core mandate and accountability.

This is a hybrid, three-times-a-week in-office role in our Jersey City Office.

Core Responsibilities

1. Stewardship Program Ownership

• Own the design, evolution, and effectiveness of the enterprise Data Stewardship Program, including:

• a clear training and enablement strategy (plan, materials, and delivery approach)

• engagement and learning cadence, including forums and structured training touchpoints

• proficiencystandards, learning progression, and measurable outcomes

• Drive steward execution and consistency through direct engagement, facilitation, and followthrough, reinforcing clear expectations and accountability for delivery.

• Support reinforcement of Data Owner accountability through preparation, facilitation, and intervention, partnering with the SVP for direct engagement when seniorlevel alignment or decision authority isrequired, and engaging more independently as experience and trust areestablished.

• Continuouslyevolvethe stewardship model, including onboarding and ongoing training plans, based on feedback, delivery signals, and changes in business or governance priorities.

• Ensure the Data Stewardship Program explicitly supports analytics and AI use cases by defining stewardship expectations for AIrelevant data sets, including data quality, lineage transparency, business context, and ongoing accountability.

2. Data Catalog & Stewardship Enablement

• Own the business metadata and lineage enablement strategy and measurable outcomes across assigned domains and initiatives.

• Establish, document, and enforce enterprise standards and best practices for metadata, lineage, and the data catalog-including definitions, documentation expectations, usage patterns, stewardship responsibilities, and support for analytics and AI use cases (e.g., explainability, provenance, andappropriate usagecontext).

• Define and reinforce clear stewardship expectations across business and technical metadata, ensuringappropriate collaborationand accountability among Business Stewards, Technical Stewards, and Data Owners.

• Accountable for defining, enforcing, and sustaining minimum data catalog completeness standards for critical data assets, including ownership, business definitions, classifications, lineage, and usage context.

• Ensure the completeness, accuracy, consistency, and ongoing maintenance of the enterprise data catalog to support reuse, singlepoint definitions, and trustworthy analytics and AI outcomes.

• Promote and reinforce active use of the data catalog as an operational governance tool for decisionmaking, data quality management, reuse, transparency, and regulatory readiness-not merely as documentation.

• Apply a trustbutverify approach byvalidatingassumptions, logic, and proposed approaches before scaling standards or practices; apply a working understanding of data quality, privacy, and analytics/AI dependencies toidentifydownstream impacts and engageappropriate accountableowners early.

• Ensure data catalog gaps (e.g., missing, unclear, or stale metadata or lineage) are explicitly tracked, assigned, and driven to closure as part of stewardship, domain, or initiative execution.

• Partner with data stewards, data, technology, and analytics teams, as well as supporting data team members, to translate governance intent into executable outcomes whilemaintainingclear ownership boundaries.

3. Forums, Facilitation & Decision Flow

• Own the purpose, decisions, and outcomes of stewardshiprelated forums and working groups.

• Personallyfacilitatesessions when:

• issues are complex or crossdomain

• decisions are stalled

• clarity or momentum is at risk

• Adapt forum structure, cadence, and materials based on effectiveness rather than static templates.

• Ensure decisions translate into clear actions, owners, and followthrough.

4. Enablement, Training & Business Adoption

• Design and deliver governance enablement, including:

• live training sessions

• handson exercises

• tool demonstrations

• Tailor enablement to audience maturity-from practitioners to senior leadership-using clear, businessforward language.

• Reinforce adoption through clarity, relevance, and credibility rather than complianceonly messaging.

• Creates highquality enablement artifacts, including presentations, stepbystep user guides, and recorded video training, and is comfortable learning new tools and features independently to translate capabilities into practical, businessready learning content.

5. Complex Data Quality Issue Facilitation

• Whendesignated, serve as Complex Data Quality Issue Facilitator, responsible for:

• driving crossdepartment coordination across impacted stewards and teams

• directing the focus of coordinated remediation efforts

• ensuring progress, accountability, and followthrough

• coordinating regular updates with impacted Data Owners

• This responsibility is assigned casebycase based on issue complexity andexpertiseneeds and may also be fulfilled by other qualified roles.

6. Initiative Delivery, Change & Performance Management

• Lead execution of stewardship, data catalog, and assigned data governance initiatives within SVPapproved scope and decision boundaries, ensuring alignment to governance intent and outcomes.

• Identify, manage, and escalate delivery, alignment, andadoptionrisks requiring reprioritization or senior intervention.

• Stay handson as needed tomaintainmomentum, including drafting materials, framing logic, capturing decisions, and driving followthrough 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, whileremainingdirectly accountable forexecutionquality, outcomes, and adoption.

• Lead initiativespecific 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 executiveready insights.

• 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 governance, stewardship, metadata, and adoption risks, ensuring material risks are captured andmaintainedin 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 areidentified, tracked, and actively managed.

Operating Expectations

• Operates effectively in a startuplike, evolving environment, adapting priorities, processes, and execution asnew informationemergeswhilemaintainingforward momentum.

• Demonstrates abuildermindset by creating clarity, structure, and momentum where they do not yet exist.

• Applies a trustbutverify approach byvalidatingassumptions, testing logic, and confirming decision boundaries before scaling solutions; surfaces misalignment early and recalibrates based onnew informationor leadership feedback.

• Leads through facilitation, presence, and followthrough rather than hierarchy, flexing comfortably between strategy, facilitation, and handson 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 constructivechallengeearly, then aligns execution once decisions are made to ensuretimely, consistent followthrough.

• Technically fluent and comfortable selflearning governance tools and capabilities, translating complex functionality into clear, businessready 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 ina relateddiscipline.

• Experience leading capabilities in data stewardship, metadata, data quality, or governanceadjacent capabilities in complex organizations, with a proven ability to deliver outcomes in ambiguous, crossfunctional environments.

• Demonstrated comfort balancing leadership accountability with handson execution, driving complex initiatives across business, data, and technology stakeholders with strong execution discipline.

• Proven ability tooperateindependently whileremainingaligned to senior leadership intent and decision boundaries, applying sound judgment,validatingassumptions, and surfacing risks or misalignment early.

• Strong written and verbal communication skills, including the ability to produce executiveready 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 followthrough, witha track recordof driving decisions to closure.

• Proficiencywith 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 applyingprevioussolutions 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 with...