AVP, Data Governance - Hybrid (Jersey City)

Core Responsibilities

• 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
• proficiency standards, 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 is required, and engaging more independently as experience and trust are established.
• Continuously evolve the 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.
• 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, and appropriate usage context).
• Define and reinforce clear stewardship expectations across business and technical metadata, ensuring appropriate collaboration and 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 by validating assumptions, logic, and proposed approaches before scaling standards or practices; apply a working understanding of data quality, privacy, and analytics/AI dependencies to identify downstream impacts and engage appropriate accountable owners 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 while maintaining clear ownership boundaries.
• Forums, Facilitation & Decision Flow
• Own the purpose, decisions, and outcomes of stewardship-related forums and working groups.
• Personally facilitate sessions 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.
• 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 language.
• Reinforce adoption through clarity, relevance, and credibility rather than compliance-only messaging.
• Create high-quality enablement artifacts, including presentations, step-by-step user guides, and recorded video training, and is comfortable learning new tools and features independently to translate capabilities into practical, business-ready learning content.
• 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
• Initiative Delivery, Change & Performance Management
• Lead execution of stewardship, data catalog, and assigned data governance 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 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 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.