Lead Specialist, Governance & Master Data

1. Why This Role Matters The Lead Specialist, Governance & Master Data strengthens Ma’aden’s ability to treat data as a trusted enterprise asset. The role establishes the standards, ownership, controls, and master data foundations required for reliable reporting, analytics, artificial intelligence, automation, and business decision-making. The role creates value by improving the accuracy, consistency, traceability, and usability of critical data across business functions and technology platforms. Strong governance and master data practices reduce operational inefficiency, limit data-related risk, improve regulatory and audit readiness, and enable teams to make decisions with greater confidence. As a specialist authority, the role turns enterprise governance requirements into practical controls, clear accountabilities, and sustainable ways of working. The role also builds a culture in which data owners, stewards, and users understand their responsibilities and actively improve the quality and value of Ma’aden’s information assets. 2. What You Will Deliver Enterprise Data Governance

• Establish governance frameworks, policies, standards, and decision rights that strengthen ownership and accountability for critical data.
• Align governance practices with enterprise data strategy, architecture principles, cybersecurity requirements, and regulatory obligations.
• Embed governance into business processes and technology delivery rather than applying controls after implementation.

Master Data Excellence

• Govern master data definitions, hierarchies, business rules, and lifecycle controls across critical domains such as assets, materials, suppliers, and employees.
• Improve master data consistency across systems to reduce duplication, reconciliation effort, and process disruption.
• Enable trusted master data that supports efficient operations, integrated reporting, analytics, and automation.

Data Quality, Metadata & Traceability

• Define measurable quality rules and controls for completeness, accuracy, consistency, validity, and timeliness.
• Drive root-cause resolution of recurring data issues and track improvements through agreed quality indicators.
• Strengthen data discoverability and trust through effective metadata, lineage, cataloguing, and traceability practices.

Governed Platforms & Digital Enablement

• Integrate data quality, ownership, security, metadata, and lineage controls into data pipelines, cloud environments, and analytics platforms.
• Enable scalable and governed data foundations for reporting, advanced analytics, AI, and digital initiatives.
• Apply automation and analytics to improve control effectiveness, issue detection, and governance efficiency.

Risk, Compliance & Assurance

• Identify and mitigate governance, privacy, security, and master data risks before they affect business performance.
• Maintain evidence-based controls that strengthen regulatory compliance, audit readiness, and accountability.
• Monitor adherence to governance standards and drive corrective action where control gaps are identified.

Adoption & Capability Building

• Equip data owners, stewards, analysts, and technology teams with practical playbooks, procedures, and governance guidance.
• Influence business and technology stakeholders to adopt master data standards and accountable data practices.
• Build sustainable governance capability through mentoring, knowledge transfer, and continuous improvement.

3. What Success Looks Like

• Critical data domains achieve agreed improvements in completeness, accuracy, consistency, validity, and timeliness.
• Master data standards and hierarchies are consistently adopted across priority systems and business processes.
• Recurring data issues, duplicate records, reconciliation effort, and control exceptions reduce over time.
• Critical data assets have clearly assigned owners and stewards, supported by documented accountability.
• Metadata, lineage, and catalogue coverage improve for priority data assets, increasing traceability and user confidence.
• Analytics, AI, reporting, and automation initiatives use governed data that meets defined quality and compliance requirements.

4. Minimum Qualifications

• Bachelor’s degree in Computer Science, Data Management, Information Systems, or a related discipline.

5. Experience

• 8–10 years of relevant experience in data governance, master data management, or enterprise data management.
• Experience implementing enterprise data governance and master data management frameworks.
• Experience establishing data quality, stewardship, metadata, lineage, or data catalogue capabilities within a complex organization.
• Experience translating governance requirements into practical business processes and technology controls.

6. Skills That Matter Functional Expertise

• Enterprise Data Governance
• Master Data Management
• Data Quality Management
• Metadata & Data Catalogue Management
• Data Lineage & Traceability
• Data Stewardship & Lifecycle Management

Business & Delivery

• Governance Framework Design
• Policy, Standards & Control Development
• Data Risk & Compliance Management
• Governance Performance Measurement
• Root-Cause Analysis & Remediation
• Continuous Improvement & Automation

People & Collaboration

• Stakeholder Management & Influence
• Business and Technology Collaboration
• Governance Advisory
• Facilitation & Communication
• Coaching & Knowledge Transfer
• Change Adoption & Accountability