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Data Architect
Location : Onsite – Riyadh
Role Duration – Long Term

Role Overview

Key Responsibilities

• Assess the current data landscape across business units and entities, including data sources, platforms, models, flows, integrations, ownership, dependencies and key pain points.
• Define enterprise data architecture principles, standards and the target-state architecture across data ingestion, integration, storage, processing, sharing, analytics and AI enablement.
• Develop conceptual and logical enterprise data models, canonical models and common definitions for priority data domains.
• Define data-domain boundaries, data-product interfaces and interoperability standards for a federated, multi-entity environment.
• Establish architecture patterns for APIs, event-driven integration, batch exchange and secure data sharing across internal and external ecosystems.
• Advise on the architecture of data lakehouse or warehouse platforms, master and reference data, metadata catalogues, lineage, data quality, security and privacy controls.
• Align the data architecture with business capabilities, application architecture, technology strategy and enterprise-wide transformation priorities.
• Define data architecture governance, decision rights, design-review gates, reusable patterns and exception-management processes.
• Identify architecture gaps, dependencies and migration priorities, and develop a practical transition roadmap.
• Facilitate architecture workshops and translate complex technical decisions into clear recommendations for senior stakeholders.

Required Experience and Qualifications

• 15+ years of relevant experience in data architecture, enterprise information architecture, data engineering or large-scale data transformation.
• Proven experience defining enterprise-wide data architecture for a large, complex organization, conglomerate, public-sector ecosystem or other multi-entity environment.
• Strong expertise in conceptual and logical data modelling, enterprise data domains, canonical models and data interoperability.
• Deep understanding of modern data platforms, including cloud or hybrid data architectures, lakehouse or warehouse patterns, data integration and analytics ecosystems.
• Strong knowledge of data governance, metadata, lineage, master data management, data quality, security and privacy-by-design principles.
• Experience establishing federated architecture standards and aligning diverse business units and entities around common enterprise patterns.
• Ability to connect data architecture decisions to business outcomes, operating-model implications, investment requirements and implementation priorities, and communicate them clearly to senior stakeholders