Data Governance Manager
Position Summary
The Manager of Data Governance leads the enterprise framework for trusted, consistent, secure, and usable data across business units, systems, and teams. This role establishes the operating model, policies, standards, ownership structure, and measurement system required to improve data quality, master data consistency, metadata discipline, lineage, stewardship, and decision confidence. The Manager partners across Technology & AI, MDM, eCommerce, ERP, CRM, Product, Sales, Operations, Finance, Compliance, and business leadership to ensure data governance is practical, scalable, and aligned to business outcomes. This is a hands-on leadership role that balances strategy with execution: building governance guardrails without slowing delivery, defining clear accountability across data owners and stewards, and ensuring critical enterprise data can support analytics, automation, AI, operational execution, and customer-facing processes.
Essential Duties & Responsibilities
Define and execute the enterprise data governance roadmap aligned to business priorities, transformation programs, analytics needs, and AI readiness.
Create and maintain governance policies, standards, decision rights, RACI models, escalation paths, and issue-resolution routines.
Lead or facilitate a cross-functional Data Governance Council to review high-impact data decisions, resolve conflicts, approve standards, and prioritize remediation work.
Translate governance strategy into practical operating rhythms, measures, and documentation that business teams can adopt consistently.
Define and operationalize the roles of Data Owners, Data Stewards, Data Custodians, Data Guardians, and domain subject matter experts.
Partner with business leaders to assign clear ownership for critical data domains and ensure accountability for quality, usage, security, and strategic alignment.
Build stewardship routines that improve issue intake, triage, root-cause analysis, remediation, exception handling, and ongoing monitoring.
Coach cross-functional teams on data ownership expectations, governance responsibilities, and decision-making processes.
Create communication and enablement materials that improve data literacy and clarify how governance supports business execution.
Drive governance for customer, supplier/vendor, product, pricing, and other critical master data domains across enterprise systems and business units.
Partner with system teams to define business rules, validation controls, survivorship logic, naming standards, deduplication rules, and data hierarchy requirements.
Support major data-dependent initiatives by ensuring data standards are defined before scale.
Ensure operational validation is incorporated into data initiatives so data performs correctly across quote, order, receive, fulfill, invoice, reporting, and customer-facing workflows.
Identify structural data-quality risks, rework drivers, and bottlenecks; recommend prioritization, remediation plans, and executive escalation where needed.
Define enterprise data quality dimensions, KPIs, scorecards, thresholds, and remediation SLAs for critical data elements.
Establish metadata and data catalog practices that document definitions, ownership, transformations, lineage, and usage context.
Implement governance controls for data classification, access, retention, privacy, auditability, integration, and lifecycle management.
Partner with Technology, Cybersecurity, Compliance, and Legal teams to ensure data governance practices align with privacy, security, regulatory, and contractual requirements.
Create executive-level reporting that communicates data quality trends, risks, progress, value realization, and decisions needed.
Ensure governed data definitions and semantic layers support consistent KPI reporting, self-service analytics, Power BI dashboards, Microsoft Fabric assets, and AI use cases.
Partner with BI, analytics, AI, automation, and business teams to define data requirements, source-of-truth logic, and controls for high-value use cases.
Support responsible AI readiness by ensuring data sources, lineage, classification, ownership, quality, and usage controls are defined for AI workflows.
Promote a business-first approach to governance by linking data quality improvements to revenue growth, margin protection, operational efficiency, customer experience, compliance, and risk reduction.
Identify opportunities to automate data validation, monitoring, exception management, and stewardship workflows.
Lead governance workstreams with clear milestones, owners, backups, priorities, risks, and decision points.
Manage stakeholder expectations with transparent timelines, level-of-effort estimates, dependencies, and escalation paths.
Prepare concise executive updates, decision memos, dashboards, and recommendations for senior leadership.
Influence without direct authority across business, technology, operations, commercial, compliance, and finance stakeholders.
Build a culture of data accountability, transparency, operational discipline, and continuous improvement.
Qualifications
Data Governance: Data quality, data cataloging, metadata, lineage, stewardship, ownership, standards, and policy execution
Master Data Management: Customer, product, supplier/vendor master data; domain-driven governance; data lifecycle and controls
Technology Partnership: ERP, PIM, CRM, eCommerce, BI/analytics, Microsoft Fabric, Power BI, integration, and semantic data layers
Leadership & Influence: Cross-functional facilitation, council leadership, conflict resolution, executive communication, and change adoption
Risk, Compliance & Security: Data classification, privacy, cybersecurity alignment, auditability, regulatory readiness, AI data-use controls
Education & Experience Requirements:
6+ years of progressive experience in data governance, master data management, data quality, data management, analytics, data architecture, or related enterprise data roles.
3+ years of leadership experience, including managing teams, leading cross-functional programs, or influencing governance forums across business and technology stakeholders.
Demonstrated experience establishing or operating data governance frameworks, stewardship programs, data ownership models, data quality scorecards, and issue-management routines.
Strong understanding of master data management concepts across product, customer, supplier/vendor, pricing, and other enterprise data domains.
Experience partnering with ERP, PIM, CRM, eCommerce, BI, data engineering, and analytics teams to define data standards and source-of-truth logic.
Ability to communicate complex data issues in business terms and prepare executive-ready recommendations, risks, tradeoffs, and decision points.
Strong facilitation, change management, prioritization, conflict-resolution, and stakeholder-management skills.
Bachelor’s degree in Information Systems, Data Management, Business Analytics, Computer Science, Engineering, or equivalent practical experience.
Preferred Qualifications:
Experience with Microsoft Fabric, Power BI, data catalogs, metadata management tools, data quality platforms, workflow tools, or modern data governance technologies.
Experience in distribution, manufacturing, B2B commerce, supply chain, product information management, or multi-business-unit environments.
Familiarity with Infor CSD, Syndigo, ROC Commerce, Dynamics 365, or similar ERP/PIM/eCommerce/CRM ecosystems.
Experience supporting AI governance, responsible AI, data classification, privacy, security, or compliance requirements for advanced analytics and automation.
Relevant certifications such as DAMA/CDMP, DCAM, Microsoft data certifications, Lean/Six Sigma, Agile/Scrum, or project/program management credentials.
What Success Looks Like:
Clear data ownership and stewardship are embedded across critical business domains, reducing ambiguity and accelerating issue resolution.
Enterprise data quality improves measurably through scorecards, controls, root-cause analysis, and remediation routines.
Governed definitions and source-of-truth logic create consistent KPI reporting across business units and functions.
Critical data-dependent initiatives move faster because standards, validation scenarios, and decision rights are defined before execution scales.
Business teams view governance as an enabler of speed, trust, AI readiness, compliance, and better decisions—not as unnecessary bureaucracy.
Leadership has transparent visibility into data risks, progress, decisions needed, and business value delivered.
Physical Requirements:
Ability to safely and successfully perform the essential job functions consistent with the ADA, FMLA and other federal, state and local standards, including meeting qualitative and/or quantitative productivity standards.
Ability to maintain regular, punctual attendance consistent with the ADA, FMLA and other federal, state, and local standards.
This position requires applicants to be authorized to work in the US without sponsorship; TestEquity does not sponsor applicants for work visas. TestEquity provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state, or local laws. This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation, and training.