The Data and AI Architect is responsible for designing, maintaining, and governing the enterprise data and AI architecture to ensure data is managed as a strategic organizational asset and AI solutions are implemented effectively, securely, and responsibly. The role develops and supports secure-by-design data and AI solutions, standards, models, frameworks, and roadmaps that enable the effective integration, management, analysis, and utilization of data across business and technology domains and comply with security requirements.
Working collaboratively with business, data, technology and risk teams, the Data and AI Architect provide architectural guidance for data platforms, data integration, business intelligence, analytics, AI and automation initiatives. The role supports the design and delivery of scalable, secure, and reliable data and AI solutions that align with business objectives, operational requirements, and regulatory expectations.
The incumbent collaborates with stakeholders to ensure data and AI capabilities are built on trusted data foundations, promotes good practices in data governance and AI governance, and evaluates emerging technologies to support innovation, continuous improvement, and data-driven decision-making across the organization.
Job Requirement
- Develop and maintain data and AI architecture standards, principles, reference models, and guidelines to support business, operational, analytical, and regulatory requirements.
- Design and maintain target-state data architectures, data models, information flows, and AI solution architectures that enable scalable, integrated, and trusted business capabilities.
- Provide architecture guidance and technical advisory support for data, analytics, artificial intelligence, and automation initiatives, ensuring alignment with approved architecture standards and technology roadmaps.
- Define and support data integration patterns and approaches to enable consistent, accurate, secure, and efficient data exchange across applications, platforms, and external ecosystems.
- Conduct architecture reviews and solution assessments to ensure compliance with enterprise architecture principles, data standards, AI governance requirements, and technology standards.
- Support the implementation and continuous improvement of data-related capabilities, including data platforms, data migration, data quality, master data management, metadata management, and data lifecycle management initiatives.
- Collaborate with business, data, technology, risk, and information security stakeholders to translate business requirements into practical, scalable, and secure data and AI architecture solutions.
- Promote and support data governance and AI governance practices, including data quality, metadata management, data lineage, information management, model governance, and responsible AI principles.
- Support analytics, reporting, machine learning, generative AI, and data-driven initiatives by ensuring the availability of reliable, accessible, and high-quality data assets.
- Evaluate emerging data and AI technologies, architecture patterns, and industry best practices, and recommend opportunities for innovation, optimization, and continuous improvement.
- Work with vendors, implementation partners, and internal technology teams to ensure delivered data and AI solutions comply with approved architecture standards, business requirements, and governance expectations.
- Ensure solution designs incorporate security-by-design, privacy-by-design, and regulatory requirements, and work closely with the Information Security and Risk functions to address security, privacy, and compliance requirements.
- Support architecture governance processes by preparing architecture artefacts, design documentation, standards, and roadmap inputs for review and approval.
- Provide technical mentoring, knowledge sharing, and guidance to project teams and technology practitioners on data and AI architecture best practices.
Qualification
- Master's degree in Data Science, Artificial Intelligence, Analytics, or a related field, with a minimum of 5–6 years of relevant experience in data, analytics, or AI delivery roles, including involvement in enterprise or transformation initiatives; OR
- Bachelor's Degree in Computer Science, Information Technology, Data Science, Artificial Intelligence, Computer Engineering, Information Systems, Software Engineering, or a related discipline, with a minimum of 8–10 years of relevant experience in data architecture, data engineering, analytics, business intelligence, AI solutions, data management, or related technology disciplines, including at least 3–5 years in a data architecture, solution architecture, technical lead, or equivalent role; OR
- Diploma in a relevant discipline, with a minimum of 10–12 years of progressive experience in data architecture, data engineering, analytics, AI solutions, data integration, data management, or enterprise data platform initiatives, including at least 5 years in a senior technical, architecture, or technology leadership role.
Preferred:
- Experience in designing and implementing enterprise data architectures, conceptual/logical/physical data models, data integration frameworks, and modern data platforms.
- Experience in data governance, master data management (MDM), metadata management, data quality, data lineage, and information management initiatives.
- Experience supporting analytics, business intelligence, data warehousing, data lake/lakehouse platforms, data visualization, and self-service analytics solutions.
- Experience in designing, integrating, or supporting Artificial Intelligence (AI), Machine Learning (ML), Generative AI (GenAI), intelligent automation, or advanced analytics solutions.
- Experience with cloud-based data and AI platforms such as Microsoft Azure, Microsoft Fabric, AWS, Google Cloud Platform (GCP), Databricks, Snowflake, or equivalent technologies.
- Experience in data migration, data modernization, platform transformation, or enterprise-wide technology transformation initiatives.
- Experience working with API integration, event-driven architecture, data pipelines, ETL/ELT processes, and enterprise integration technologies.
- Experience collaborating with business, technology, risk, governance, and information security stakeholders in delivering data and AI solutions.
- Experience within the financial services, insurance, takaful, banking, or other highly regulated industries would be an added advantage.
