Principal Specialist Data Modeling & Arch Enterprise

کام کیا ہوگا

About the Role Ma’aden is seeking a Principal Specialist, Data Modeling & Arch Enterprise . This full-time role involves leading the design, modeling, and evolution of enterprise data architecture. The objective is to ensure that data products, curated layers, and integration patterns are standardized, scalable, high-quality, and governed across the organization. The specialist will define how operational and corporate data should be modeled, aligned, and transformed to support analytics, reporting, AI enablement, and trusted downstream consumption across various business domains. Key Responsibilities 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. Establish enterprise modeling standards, naming conventions, 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. 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. 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. 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. Qualifications and Experience A minimum of 10 years of relevant experience in data modeling and enterprise architecture. Outcomes of Success Successful performance in this role will be demonstrated by enterprise conceptual, logical, and physical data models that are standardized, scalable, reusable, and aligned with business domains. Canonical models, conformed dimensions, reference data structures, and naming standards will improve semantic consistency across domains. Curated layers and data product schemas will support trusted reporting, analytics, AI enablement, and downstream consumption. Metadata, lineage, data quality, stewardship, and governance expectations will be embedded into modeling and integration patterns. Delivery teams will receive strong modeling assurance, coaching, and practical design guidance, leading to reduced duplication and improved reuse.