Senior Specialist, Data Base & Data Mart

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 Senior Specialist, Data Base & Data Mart enables trusted, scalable, and high-performance data foundations that support analytics, reporting, AI, and business decision-making across Maaden. The role ensures enterprise data assets are structured, governed, and optimized to deliver reliable insights that improve operational and strategic outcomes. The role strengthens Maaden’s Data & AI ecosystem by translating complex business and analytical requirements into robust database and data mart solutions. Through technical leadership and engineering excellence, it improves data accessibility, quality, performance, and governance while ensuring enterprise standards are consistently applied. By providing expertise in data architecture, database optimization, and lifecycle management, the role enables data-driven transformation initiatives and ensures that enterprise data platforms remain secure, resilient, and aligned with long-term business objectives. 2. What You Will Deliver Database & Data Mart Architecture - Deliver scalable and high-performing database and data mart solutions that support enterprise analytics, reporting, and AI initiatives. - Translate business and analytical requirements into logical and physical data models aligned with enterprise architecture standards. - Establish reusable design patterns that improve consistency, maintainability, and long-term scalability. Data Integration & Solution Delivery - Enable reliable delivery of enterprise data through robust ETL/ELT processes, transformation logic, and data integration practices. - Ensure solutions are deployed, tested, and transitioned into production effectively and sustainably. - Improve delivery quality through technical reviews, architecture compliance, and engineering best practices. Performance & Operational Reliability - Optimize database and data mart performance through tuning, workload analysis, indexing, and capacity management. - Strengthen operational resilience through effective monitoring, issue resolution, recovery planning, and preventive improvements. - Improve platform availability and reliability while supporting growth in business and analytical demand. Data Governance & Security - Embed data quality, lineage, metadata, ownership, retention, and governance requirements within enterprise data solutions. - Ensure data assets remain secure, auditable, and compliant with corporate policies and regulatory requirements. - Reduce data risk through proactive assessment and remediation of control gaps and quality issues. Stakeholder Engagement & Delivery Excellence - Partner with engineering, analytics, governance, architecture, cybersecurity, and business teams to deliver prioritized data capabilities. - Provide technical guidance that improves delivery decisions, solution quality, and stakeholder confidence. - Communicate technical recommendations, trade-offs, and constraints in a clear and business-focused manner. Continuous Improvement & Capability Building - Drive adoption of modern database, cloud, automation, and observability practices that enhance value and efficiency. - Strengthen engineering capability through standards, reusable assets, mentoring, and knowledge sharing. - Improve delivery consistency and operational maturity through continuous enhancement initiatives. 3. What Success Looks Like - Enterprise databases and data marts consistently deliver reliable, accurate, and timely data for analytics and business reporting. - Data performance, availability, and scalability improve through proactive optimization and engineering best practices. - Data governance requirements including lineage, metadata, ownership, and quality controls are embedded across solutions. - Production incidents and performance issues are resolved effectively with reduced recurrence. - Stakeholders have confidence in the quality, accessibility, and reliability of enterprise data assets. - Modern data engineering practices improve delivery efficiency, maintainability, and long-term platform sustainability. 4. Minimum Qualifications - Bachelor’s degree in Computer Science, Data Engineering, Information Systems, or a related discipline. - Master’s degree in Data Engineering, Data Architecture, Analytics, or a related field is advantageous. 5. Experience - 7–10 years of progressive experience in database development, data engineering, data warehousing, busine

Minimum Qualifications: Bachelor’s degree in Computer Science, Data Engineering, Information Systems, or a related discipline. Master’s degree in Data Engineering, Data Architecture, Analytics, or a related field is advantageous. Experience: 7–10 years of progressive experience in database development, data engineering, data warehousing, business intelligence, and related areas.

Database & Data Mart Architecture: Deliver scalable and high-performing database and data mart solutions that support enterprise analytics, reporting, and AI initiatives. Translate business and analytical requirements into logical and physical data models aligned with enterprise architecture standards. Establish reusable design patterns that improve consistency, maintainability, and long-term scalability. Data Integration & Solution Delivery: Enable reliable delivery of enterprise data through robust ETL/ELT processes, transformation logic, and data integration practices. Ensure solutions are deployed, tested, and transitioned into production effectively and sustainably. Improve delivery quality through technical reviews, architecture compliance, and engineering best practices. Performance & Operational Reliability: Optimize database and data mart performance through tuning, workload analysis, indexing, and capacity management. Strengthen operational resilience through effective monitoring, issue resolution, recovery planning, and preventive improvements. Improve platform availability and reliability while supporting growth in business and analytical demand. Data Governance & Security: Embed data quality, lineage, metadata, ownership, retention, and governance requirements within enterprise data solutions. Ensure data assets remain secure, auditable, and compliant with corporate policies and regulatory requirements. Reduce data risk through proactive assessment and remediation of control gaps and quality issues. Stakeholder Engagement & Delivery Excellence: Partner with engineering, analytics, governance, architecture, cybersecurity, and business teams to deliver prioritized data capabilities. Provide technical guidance that improves delivery decisions, solution quality, and stakeholder confidence. Communicate technical recommendations, trade-offs, and constraints in a clear and business-focused manner. Continuous Improvement & Capability Building: Drive adoption of modern database, cloud, automation, and observability practices that enhance value and efficiency. Strengthen engineering capability through standards, reusable assets, mentoring, and knowledge sharing. Improve delivery consistency and operational maturity through continuous enhancement initiatives.