Principal Specialist, Data Modeling & Arch (Operational Technology)

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. Why This Role Matters This role establishes the data modeling and architecture foundation for Operational Technology (OT) and industrial data across plants, sites, and operational systems. It enables trusted OT analytics by standardizing how assets, tags, events, alarms, measurements, production contexts, historians, SCADA/DCS, condition monitoring, and maintenance data are represented, governed, and aligned with enterprise IT data products. What You Will Deliver OT Data Modeling Standards & Architecture - Define enterprise OT data modeling standards for time-series data, events, alarms, assets, tags, measurements, and production contexts. - Establish canonical OT concepts including asset hierarchy, location hierarchy, tag naming, measurement definitions, and work order linkage. - Design scalable data schemas for curated OT layers and reusable OT data products with auditability, lineage, and quality attributes. - Ensure modeling patterns address high-volume industrial data, latency requirements, site variability, and intermittent connectivity. OT-to-IT Data Alignment & Integration - Architect OT-to-IT data alignment patterns that connect operational measurements with enterprise entities for reporting and analytics. - Align historians, SCADA/DCS, condition monitoring, maintenance, reliability, and production data with enterprise data products. - Define integration rules for tag mapping, unit standardization, reference data, semantic consistency, and cross-site normalization. - Support cross-domain analytics by ensuring OT data is usable for reliability, production performance, safety, sustainability, and cost insights. Industrial Data Governance & Quality - Embed governance-by-design into OT data models, including ownership, metadata, lineage, quality rules, and lifecycle controls. - Define quality attributes for historian data, including compression, interpolation, quality flags, sampling frequency, and streaming patterns. - Establish reusable governance patterns for site onboarding, data standardization, naming conventions, and master/reference data alignment. - Ensure OT data products are traceable, reliable, maintainable, and aligned with platform architecture standards. Security, Compliance & Solution Design Authority - Partner with OT engineering, cybersecurity, data platform, and infrastructure teams to design secure and compliant OT data flows. - Ensure OT data architecture respects segmentation, access control, operational resilience, and industrial cybersecurity requirements. - Review solution designs, data models, vendor deliverables, and integration approaches to ensure architectural consistency. - Resolve modeling trade-offs and guide design decisions to support long-term scalability, maintainability, and operational reliability. Technical Leadership, Coaching & Knowledge Transfer - Provide technical leadership on OT semantics, industrial data architecture, data modeling practices, and data product design. - Coach engineers, data modelers, platform teams, and solution teams on OT modeling standards and implementation practices. - Lead design reviews for critical OT data initiatives and ensure decisions are documented and reusable across sites. - Build knowledge assets, standards, templates, and reference patterns to accelerate consistent OT data architecture delivery. What Success Looks Like - Standard OT data models are defined and adopted consistently across sites, platforms, and industrial systems. - Assets, tags, events, alarms, measurements, and production contexts are represented consistently for trusted analytics. - OT-to-IT integration patterns enable reliable cross-domain reporting, performance analysis, and data product development. - Curated OT layers include clear lineage, quality rules, auditability, and governance controls. - Site onboarding, tag normalization, unit standardization, and data model reuse are accelerated through practical reference patterns. Minimum Qualifications - Bachelor’s Degree in Computer Science, Computer Engineering, Data Engineering, Industrial Engineering, Information Systems, or a related discipline. Master’s Degree is preferred. Experience - Minimum 10 years of progressive experience in data modeling, data architecture, data engineering, industrial data platforms, or OT/IT integration. - Strong exposure to operational technology data sources such as historians

Minimum Qualifications: Bachelor’s Degree in Computer Science, Computer Engineering, Data Engineering, Industrial Engineering, Information Systems, or a related discipline. Master’s Degree is preferred. Experience: Minimum 10 years of progressive experience in data modeling, data architecture, data engineering, industrial data platforms, or OT/IT integration. Strong exposure to operational technology data sources such as historians.

OT Data Modeling Standards & Architecture: Define enterprise OT data modeling standards for time-series data, events, alarms, assets, tags, measurements, and production contexts. Establish canonical OT concepts including asset hierarchy, location hierarchy, tag naming, measurement definitions, and work order linkage. Design scalable data schemas for curated OT layers and reusable OT data products with auditability, lineage, and quality attributes. Ensure modeling patterns address high-volume industrial data, latency requirements, site variability, and intermittent connectivity. OT-to-IT Data Alignment & Integration: Architect OT-to-IT data alignment patterns that connect operational measurements with enterprise entities for reporting and analytics. Align historians, SCADA/DCS, condition monitoring, maintenance, reliability, and production data with enterprise data products. Define integration rules for tag mapping, unit standardization, reference data, semantic consistency, and cross-site normalization. Support cross-domain analytics by ensuring OT data is usable for reliability, production performance, safety, sustainability, and cost insights. Industrial Data Governance & Quality: Embed governance-by-design into OT data models, including ownership, metadata, lineage, quality rules, and lifecycle controls. Define quality attributes for historian data, including compression, interpolation, quality flags, sampling frequency, and streaming patterns. Establish reusable governance patterns for site onboarding, data standardization, naming conventions, and master/reference data alignment. Ensure OT data products are traceable, reliable, maintainable, and aligned with platform architecture standards. Security, Compliance & Solution Design Authority: Partner with OT engineering, cybersecurity, data platform, and infrastructure teams to design secure and compliant OT data flows. Ensure OT data architecture respects segmentation, access control, operational resilience, and industrial cybersecurity requirements. Review solution designs, data models, vendor deliverables, and integration approaches to ensure architectural consistency. Resolve modeling trade-offs and guide design decisions to support long-term scalability, maintainability, and operational reliability. Technical Leadership, Coaching & Knowledge Transfer: Provide technical leadership on OT semantics, industrial data architecture, data modeling practices, and data product design. Coach engineers, data modelers, platform teams, and solution teams on OT modeling standards and implementation practices. Lead design reviews for critical OT data initiatives and ensure decisions are documented and reusable across sites. Build knowledge assets, standards, templates, and reference patterns to accelerate consistent OT data architecture delivery.