Job Description
\n The Head of Data Governance Practice Leader is a senior leadership role responsible for defining and executing the USDI data governance strategy to ensure data is trusted, secure, compliant, and effectively leveraged as a strategic asset. This role establishes governance frameworks, drives policy adoption, and builds a federated governance operating model aligned to data domains and data products. The Head of Data Governance partners across business, technology, risk, and analytics to enable high-quality, AI-ready data while ensuring regulatory compliance and scalable governance practices.\n\n\n
Core Responsibilities:
\n
\n
A. Governance Strategy & Operating Model
\n
- \n
- \n
Define and lead the USDI data governance strategy, aligned to business priorities and data strategy
- \n
Design and implement a federated governance model (domain-based ownership, stewardship, councils)
- \n
Establish roles, responsibilities, and decision rights (Data Owners, Data Stewards, Domain Leaders)
- \n
Drive alignment across business and technology to ensure clear ownership and accountability
\n
\n
\n
\n
\n
\n
B. Policy, Standards, and Controls
\n
- \n
- \n
Own data governance policies, standards, and frameworks
- \n
Ensure consistent adoption of: Data classification & privacy controls Access and usage policies Retention and lifecycle standards
- \n
Partner with Risk, Legal, and Compliance to ensure alignment with regulatory requirements
- \n
Governance function is responsible for design, implementation, and enterprise coordination of governance standards and capabilities
\n
\n
\n
\n
\n
\n
C. Data Quality & Issue Management
\n
- \n
- \n
Establish a data quality framework with KPIs, controls, and monitoring
- \n
Drive enterprise-wide data issue management lifecycle: Intake → triage → root cause analysis → remediation → closure
- \n
Define accountability model for data defects and control ownership
- \n
Governance roles ensure data accuracy, consistency, and alignment with business objectives
\n
\n
\n
\n
\n
\n
D. Metadata, Lineage & Transparency
\n
- \n
- \n
Establish and scale enterprise capabilities for: Business glossary and definitions Metadata management Data lineage and traceability
- \n
Ensure data is: Discoverable Interpretable Trusted across domains
- \n
Governance requires metadata, lineage, and data standards to support enterprise usage
\n
\n
\n
\n
\n
E. Data Domain Governance & Stewardship
\n
- \n
- \n
Stand up and lead Data Governance Councils / Working Groups
- \n
Ensure domain-level governance execution via: Data Domain Leaders Data Stewards
- \n
Drive adoption of governance practices at the data product level
\n
\n
\n
\n
\n
F. Governance Enablement & Adoption
\n
- \n
- \n
Drive data literacy, training, and change management
- \n
Define measurable outcomes for governance adoption: Data quality improvements Metadata completeness Reduction in data issues
- \n
Partner with Data Product, Engineering, and Analytics teams to embed governance in workflows
\n
\n
\n
\n
\n
G. Platform & Tooling Alignment
\n
- \n
- \n
Oversee governance tooling strategy (e.g., catalog, lineage, data quality platforms)
- \n
Ensure governance capabilities are embedded into the data platform and pipelines
- \n
Partner with engineering to translate policies into enforceable controls
\n
\n
\n
\n
\n
H. Executive Stakeholder Management
\n
- \n
- \n
Provide executive visibility into: Data quality health Governance maturity Risk exposure
- \n
Lead cross-functional governance forums (e.g., Data SteerCo)
- \n
Influence senior leadership decision-making on data priorities
\n
\n
\n
\n
\n
2. Required Qualifications
\n
- \n
- \n
12–15+ years in data governance, data management, or data strategy leadership
- \n
Proven experience building enterprise-scale governance programs
- \n
Deep expertise in: Data governance frameworks (DAMA, DCAM, etc.) Data quality, metadata, and lineage Regulatory and risk environments (ideally FS)
- \n
Strong leadership across business and technology organizations
- \n
Experience in domain-driven / data product operating models
\n
\n
\n
\n
\n
\n
\n
3. Preferred Qualifications
\n
- \n
- \n
Experience with enterprise data platforms and tools (e.g., Collibra, Alation, Purview)
- \n
Familiarity with AI/ML data governance and model data controls
- \n
Background in financial services or regulated industries
\n
\n
\n
\n
Qualifications
\n
- \n
- \n
Minimum of eight years of related work experience.
- \n
Undergraduate degree or equivalent combination of training and experience. Graduate degree preferred.
\n
\n
\n
Special Factors
\n
Sponsorship
Vanguard is not offering visa sponsorship for this position.\n
\n
About Vanguard
\n
At Vanguard, we don't just have a mission—we're on a mission.
\n
\n
To work for the long-term financial wellbeing of our clients. To lead through product and services that transform our clients' lives. To learn and develop our skills as individuals and as a team. From Malvern to Melbourne, our mission drives us forward and inspires us to be our best.
\n
\n
How We Work
\n
Vanguard has implemented a hybrid working model for the majority of our crew members, designed to capture the benefits of enhanced flexibility while enabling in-person learning, collaboration, and connection. We believe our mission-driven and highly collaborative culture is a critical enabler to support long-term client outcomes and enrich the employee experience.
\n