Lead Specialist, Governance & Master Data

Maaden, established in 1997, is one of the fastest-growing mining companies in the world and the largest multi-commodity mining and metals company in the Middle East. We are leading the development of the mining industry to become the third pillar of Saudi Arabia’s economy by building a world-class, unique, and fully integrated mining value chain. We are pleased to share an exciting opportunity for the below mentioned position. This role offers a chance to contribute to our ambitious growth and play a key part in shaping the future of mining in the Kingdom. 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 managem

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

Enterprise Data Governance: Establish governance frameworks, policies, standards, and decision rights; align governance with enterprise data strategy, architecture, cybersecurity and regulatory obligations; embed governance into business processes and technology delivery. Master Data Excellence: Govern master data definitions, hierarchies, business rules, and lifecycle controls across assets, materials, suppliers, and employees; improve master data consistency across systems; enable trusted master data for operations, reporting, analytics, and automation. Data Quality, Metadata & Traceability: Define quality rules for completeness, accuracy, consistency, validity, timeliness; drive root-cause resolution of data issues; strengthen metadata, lineage, cataloguing, and traceability. Governed Platforms & Digital Enablement: Integrate quality, ownership, security, metadata, and lineage controls into data pipelines, cloud environments, and analytics platforms; enable scalable and governed data foundations for reporting, analytics, AI, and digital initiatives; apply automation to improve control effectiveness. Risk, Compliance & Assurance: Identify and mitigate governance, privacy, security, and master data risks; maintain evidence-based controls for regulatory compliance and audit readiness; monitor adherence to governance standards and drive corrective action. Adoption & Capability Building: Equip data owners, stewards, analysts, and technology teams with playbooks and procedures; influence stakeholders to adopt master data standards; build governance capability through mentoring and continuous improvement.