AVP, Data Governance - Hybrid (Jersey City)

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 follow‑through, 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 senior‑level 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 AI‑relevant 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, single‑point definitions, and trustworthy analytics and AI outcomes.

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

• Apply a trust‑but‑verify 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 stewardship‑related forums and working groups.

• Personallyfacilitatesessions when:

• issues are complex or cross‑domain

• 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 follow‑through.

4. Enablement, Training & Business Adoption

• Design and deliver governance enablement, including:

• live training sessions

• hands‑on exercises

• tool demonstrations

• Tailor enablement to audience maturity—from practitioners to senior leadership—using clear, business‑forward lan