Principal Specialist, Data Modeling & Arch Enterprise

Ma'aden Aluminium Company (MAC); Medina; Vor Ort; SAR 350.000 - 650.000; Vollzeit

Zusammenfassung

Ma'aden Aluminium Company (MAC) seeks a senior data architect to lead the design modeling and evolution of enterprise data architecture, ensuring standardized, scalable, high-quality data products across domains.

You will define canonical models, conformed dimensions, curated layer schemas, and governance processes, partnering with data governance and engineering teams to enable trusted analytics, AI enablement, and cross-domain interoperability.

Qualifikationen

• Bachelor’s or higher in CS/IS/Analytics with extensive data modeling experience.
• 10–15 years in data modeling, data architecture, and enterprise information architecture.
• Experience defining modeling standards, metadata, lineage, governance controls and quality assurance.
• Ability to design canonical models, conformed dimensions, and reusable data patterns.

Aufgaben

• Lead enterprise data modeling across conceptual, logical, and physical layers.
• Define canonical models, conformed dimensions, master reference data, and reusable patterns.
• Design data product schemas and curated layer schemas for analytics, AI use cases, and downstream integrations.
• Establish architecture standards and governance with data governance teams and stakeholders.
• Mentor data modelers and review modeling deliverables to ensure consistency and quality.

Kenntnisse

Data modeling

Data architecture

Canonical models

Dimensional modeling

Metadata governance

Ausbildung

Bachelor's degree in related field

Master’s degree preferred

Tools

Relational databases

NoSQL databases

Time-series databases

Lakehouse/Warehouse

ETL/ELT pipelines

Jobbeschreibung

Why This Role Matters

This role leads the design modeling and evolution of enterprise data architecture to ensure data products curated layers and integration patterns are standardized scalable high-quality and governed It defines how operational and corporate data should be modeled aligned and transformed to support analytics reporting AI enablement and trusted downstream consumption across business domains

What You Will Deliver

Lead Enterprise Data Modeling Develop and maintain conceptual logical and physical data models aligned with enterprise business domains and data architecture principles Define canonical models conformed dimensions master reference data structures and reusable modeling patterns for cross-domain consistency Design data product models and curated layer schemas that support enterprise reporting analytics AI use cases and downstream integrations Ensure models address performance maintainability scalability auditability lineage and future reuse across platforms and domains Define Architecture Standards and Governance Establish enterprise modeling standards naming conventions modeling guidelines reusable templates and quality expectations for delivery teams Partner with data governance teams to align business glossary definitions stewardship ownership metadata lineage and critical data elements Embed governance-by-design into data models curated layers transformation logic data quality controls and consumption patterns Review and approve modeling deliverables from internal teams and external vendors to ensure architectural consistency and quality Shape Data Product and Curated Layer Architecture Lead the creation and optimization of data product schemas curated layers semantic structures and consumption-ready models Define modeling approaches for relational non-relational time-series streaming lakehouse warehouse and analytical platform requirements Ensure data models support time-variant structures historical traceability auditability semantic consistency and trusted analytics consumption Coordinate with data engineering platform analytics AI and enterprise architecture teams to ensure models are technically feasible and reusable Enable Integration Interoperability and Trusted Consumption Define integration patterns that ensure consistent transformation logic metadata lineage and data quality across source-to-consumption pipelines Align operational corporate and analytical data structures to enable reliable reporting analytics AI enablement and downstream system integration Identify and resolve modeling inconsistencies duplication semantic gaps and integration challenges across domains and platforms Ensure data is modeled for trusted consumption by business users data engineers analysts data scientists and application teams Provide Technical Leadership and Modeling Assurance Act as a senior technical authority for data modeling and architecture decisions across enterprise data initiatives Mentor data modelers data engineers analysts and delivery teams on modeling standards patterns review practices and quality expectations Lead model reviews resolve design trade-offs and guide teams on semantic consistency reusability performance and maintainability Document architecture decisions model assumptions standards exceptions and reusable design guidance for enterprise adoption

What Success Looks Like

Enterprise conceptual logical and physical data models are standardized scalable reusable and aligned with business domains Canonical models conformed dimensions reference data structures and naming standards improve semantic consistency across domains Curated layers and data product schemas support trusted reporting analytics AI enablement and downstream consumption Metadata lineage data quality stewardship and governance expectations are embedded into modeling and integration patterns Delivery teams receive strong modeling assurance coaching and practical design guidance that reduces duplication and improves reuse

Skills That Matter

Enterprise data modeling conceptual logical physical canonical dimensional semantic and reference data models Data architecture patterns for curated layers data products lakehouse warehouse relational non-relational streaming and time-series platforms Data governance metadata lineage stewardship business glossary alignment data quality and auditability Conformed dimensions master and reference data semantic consistency naming standards and reusable modeling templates Source-to-consumption pipelines transformation logic integration design interoperability and trusted analytics consumption Model performance optimization scalability maintainability lifecycle management and architectural assurance Ability to review vendor and internal modeling deliverables and guide multidisciplinary delivery teams Clear communication of modeling decisions architecture trade-offs standards risks and design recommendations

Minimum Qualifications

Bachelor s Degree in Computer Science, Computer Engineering, Data Engineering, Information Systems, Software Engineering, Analytics, or a related discipline. Master s Degree in Data Architecture, Software Engineering, Analytics, or a related field is preferred. Experience Minimum 10 15 years of experience in data modeling, data architecture, enterprise information architecture, data engineering, or data platform delivery. Strong experience developing conceptual, logical, and physical models, canonical models, conformed dimensions, curated layer schemas, and reusable data product structures. Demonstrated experience defining modeling standards, data architecture patterns, metadata, lineage, governance controls, and quality assurance for enterprise data initiatives.