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Job Description:

HEAD OF DATA AND DIGITAL ARCHITECTURE 

Job Description | Updated to include AI enablement responsibilities and clarify boundaries with Data Analytics 

Job Title 

Head of Data and Digital Architecture 

Job Level 

To be evaluated 

Function 

Data and Digital Architecture 

Department 

To be confirmed 

Manager 

Chief Business Development and Transformation Officer 

Location 

Philippines 

Direct Reports 

To be confirmed 

Effective Date 

To be confirmed 

Job Purpose 

Lead the enterprise data foundation, artificial intelligence enablement, and digital architecture capability, combining data strategy, governance, architecture, engineering, integration, AI platform enablement, and technical design assurance into a coherent function. The role is accountable for making enterprise data trusted, secure, accessible, and reusable; establishing the data, platform, architecture, and control foundations required for analytics and AI; defining target-state data, AI, and digital architectures; and translating approved business and transformation requirements into scalable, integrated, resilient, secure, and supportable solutions. The role provides the governed data, approved platforms, reusable AI services, architecture standards, and operational controls required by the separate Data Analytics function. The Data Analytics function retains ownership of analytics and AI strategy, business use cases, analytical and AI models, business intelligence delivery, adoption, and measured business impact. 

Major Accountabilities 

Accountability 

% Time 

Enterprise Data and AI Foundation Strategy 

• Define and execute the enterprise roadmap for data governance, data and AI architecture, platforms, engineering, integration, metadata, knowledge assets, and master and reference data. 

• Establish the target-state data and AI enablement operating model, technical capability roadmap, investment priorities, sourcing approach, and service measures. 

• Define enterprise platform and data-readiness standards required to support machine learning, generative AI, intelligent automation, and future AI-enabled solutions. 

• Align priorities with transformation, technology modernization, regulatory obligations, and Data Analytics requirements without owning the analytics and AI portfolio, business use cases, model outcomes, or business-value commitments. 

20% 

Data, AI and Digital Solution Architecture 

• Own target-state data, AI enablement, and digital architecture and translate approved requirements into end-to-end designs across applications, data, integration, cloud, security, infrastructure, identity, digital channels, and operational support. 

• Define architecture principles, standards, reference patterns, reusable components, guardrails, and transition architectures for cloud, hybrid, and on-premise environments. 

• Define architecture patterns for machine learning, generative AI, retrieval-augmented generation, enterprise knowledge services, AI agents, intelligent automation, human oversight, and secure integration with enterprise systems. 

• Lead architecture reviews and design assurance for interoperability, scalability, resilience, security, privacy, performance, supportability, data provenance, and alignment with enterprise and regional standards. 

• Guide build-versus-buy decisions, platform selection, proofs of concept, vendor designs, technical trade-offs, architecture roadmaps, and technical-debt management. 

• Ensure traceability from approved requirements to solution components, data flows, model and service interfaces, controls, non-functional requirements, dependencies, and operational ownership. 

25% 

Data and AI Platforms, Engineering and Integration 

• Lead design, delivery, and optimization of data and AI enablement platforms, lakes and warehouses, reusable data products, pipelines, interfaces, and batch, real-time, or event-driven integration. 

• Provide approved technical capabilities for model development and deployment, model and prompt services, feature management, vector search, knowledge repositories, model gateways, monitoring, and secure AI integration, in partnership with Technology and Data Analytics. 

• Set engineering standards for modeling, metadata, lineage, data and model provenance, testing, deployment automation, observability, reliability, production support, and cost management. 

• Ensure availability, quality, timeliness, security, and controlled access for data and platform services used by operational systems, enterprise reporting, Data Analytics, and authorized AI solutions. 

• Drive modernization of legacy data assets with business-continuity, migration-control, and transition-architecture discipline. 

25% 

Data and Responsible AI Governance, Quality, Privacy and Risk 

• Establish frameworks for data ownership, stewardship, quality, metadata, lineage, classification, retention, access, sharing, and lifecycle management. 

• Establish the technical governance and control framework for responsible AI, including approved platforms, model and service inventory, data provenance, access controls, security testing, monitoring, human oversight, explainability enablement, auditability, and lifecycle controls. 

• Partner with Risk, Compliance, Privacy, Cybersecurity, Legal, Records Management, Internal Audit, Technology, and Data Analytics to embed obligations and controls into data, AI, and digital solution designs. 

• Define critical data elements, quality rules, control evidence, technical issue management, and remediation priorities; escalate material risks and unresolved ownership issues. 

• Provide governed, documented, and quality-assured data and AI platform services to authorized consumers while preserving formal business-data ownership and model-accountability boundaries. 

15% 

Architecture Delivery Governance and Technical Assurance 

• Govern data, AI, and digital designs through architecture checkpoints from concept and option assessment through detailed design, implementation, transition, and post-implementation validation. 

• Maintain architecture decisions, design exceptions, conformance evidence, reusable patterns, and remediation plans for material technical and AI architecture risks. 

• Coordinate technical dependencies across business, technology, digital, data, security, infrastructure, regional teams, Data Analytics, and delivery partners. 

• Assure vendor and system-integrator deliverables for technical quality, secure AI design, knowledge transfer, operational readiness, and compliance with approved architecture. 

10% 

Leadership and Capability Building 

• Build and lead multidisciplinary capability spanning data architecture, solution architecture, AI architecture enablement, data engineering, integration, governance, and platform operations. 

• Communicate architecture decisions, progress, technical risks, AI dependencies, and service performance to executive, business, technology, and regional stakeholders. 

• Develop architecture, engineering, and AI platform capability, succession, communities of practice, and disciplined collaboration with Data Analytics and Technology. 

5% 

Specialized Knowledge and Technical Competencies 

• Enterprise data strategy, data and AI operating models, platform roadmaps, architecture governance, and technical service management. 

• Solution architecture methods, reference architectures, capability mapping, integration patterns, application programming interfaces, event-driven architecture, identity, and non-functional requirements. 

• Modern cloud and hybrid data platforms; lake, lakehouse, and warehouse patterns; batch and streaming processing; and extract-load-transform or extract-transform-load pipelines. 

• AI platform architecture, machine learning operations, large language model integration, retrieval-augmented generation, vector databases, knowledge repositories, model gateways, prompt and model services, AI observability, and secure AI integration patterns. 

• Data engineering delivery disciplines, including infrastructure as code, continuous integration and deployment, automated testing, observability, reliability engineering, and cost optimization. 

• Conceptual, logical, and physical data modeling; metadata, lineage, cataloging, master and reference data, data and model provenance, information lifecycle, and semantic-layer enablement. 

• Data governance, quality, privacy, cybersecurity, records retention, access control, third-party risk, model-risk interfaces, and regulatory compliance in a regulated environment. 

• Working knowledge of analytics, machine learning, and generative AI requirements sufficient to enable Data Analytics, without ownership of analytical methods, model development, use-case selection, adoption, or business-impact measurement. 

Problem Solving 

• Resolves complex issues involving legacy platforms, fragmented data ownership, emerging AI technologies, competing priorities, evolving requirements, and regulatory constraints. 

• Makes architecture and investment trade-offs across speed, value, cost, security, resilience, scalability, vendor dependency, technical debt, and maintainability. 

• Determines root causes and remediation for material data-quality, integration, AI platform, performance, reliability, and control issues spanning systems and business units. 

• Provides technical direction when requirements are incomplete, AI technologies are emerging, or vendors propose competing approaches. 

Education and Experience 

• Bachelor's degree in Computer Science, Information Systems, Engineering, Data Science, or a related field; relevant postgraduate degree preferred. 

• Typically 12 or more years of progressive experience across architecture, data management, engineering, integration, AI platform enablement, or technology delivery, including substantial leadership responsibility. 

• Demonstrated success delivering enterprise-scale data platforms, AI enablement capabilities, and digital solution architectures across business and technology teams. 

• Strong technical leadership credibility in solution architecture, data architecture, cloud and hybrid platforms, integration, data engineering, governance, AI platforms, and technical delivery. 

• Experience in insurance, banking, financial services, or another highly regulated environment strongly preferred. 

• Relevant certifications in cloud architecture, data management, enterprise architecture, security, AI platforms, or engineering are advantageous. 

Communication Scope 

• Internal: Executive leadership, Technology, Enterprise Architecture, Digital, Product, Operations, Distribution, Finance, Actuarial, Data Analytics, Risk, Compliance, Privacy, Cybersecurity, Legal, Internal Audit, Procurement, and regional teams. 

• External: Regulators and auditors, technology, data, and AI vendors, cloud and platform providers, system integrators, and consultants, as required. 

• Purpose: Strategic alignment, solution design and approval, technical prioritization, AI enablement, risk and control oversight, delivery governance, and service enablement. 

Job Category:

Posting End Date: