Enterprise Data & AI Integration Architect

With 75 years of experience, our focus is on helping the most vulnerable children overcome poverty and experience fullness of life. We help children of all backgrounds, even in the most dangerous places, inspired by our Christian faith.

Come join our 31,000+ staff working in nearly 100 countries and share the joy of transforming vulnerable children’s life stories!

Employee Contract Type:

Job Description:

IMPORTANT INFORMATION:

• All CVs should be submitted in English.

• This position is open to candidates based in countries where World Vision International is legally registered to operate.

JOB PURPOSE :

The Enterprise Data & AI Integration Architect defines and evolves the organization's target-state data, integration, analytics, and AI architecture. The role connects business strategy to executable technology choices across data platforms, integration, information architecture, governance, analytics, machine learning, generative AI, and knowledge systems. The role operates across all architecture domains: Business, Data, Application, and Technology (BDAT), with particular depth in Data Domain. Strategic enough to shape investment and governance, and hands-on enough to validate designs, prototype critical patterns, lead complex global solution design and guide delivery teams.

Acting as a trusted advisor and design authority, the architect establishes reusable standards and guardrails; leads current-state, target-state, and transition planning; assures major solution designs; and enables teams to deliver secure, interoperable, cost-effective, and AI-ready capabilities.

The architect provides technical leadership and coordination across a defined portfolio, programme, or architectural domain, segment or global platforms, ensuring consistency, quality, and alignment of solution architectures within their scope, translating enterprise direction into practical guidance for delivery teams. This role bridges enterprise architecture intent and solution execution.

Within the data specialization, the role provides architectural leadership for enterprise data design, data management, and data governance, ensuring solutions align with data standards, regulatory requirements, and approved enterprise architecture direction. This specialization focuses on embedding sound data architecture practices across initiatives while supporting consistent, scalable, and sustainable technology outcomes.

Role mandate:

• Create a coherent enterprise data and AI architecture blueprint, with a pragmatic multi-year roadmap from current state to target state, to achieve and document NorthStar Architecture vision.

• Modernize the data landscape so governed, high-quality data can support operational use cases, analytics, machine learning, generative AI, and agentic workflows.

• Reduce fragmentation, duplicated platforms, tech debt, inconsistent definitions, uncontrolled data movement, and architecture debt in partnership with data governance.

• Embed security, privacy, resilience, regulatory compliance, responsible AI, observability, and cost management into architecture by design.

• Increase delivery speed through reference architectures, approved patterns, data products, reusable components, and clear engineering standards.

• Translate complex architectural trade-offs into decisions that executives, business leaders, risk teams, product owners, and engineers can act on.

• Ensure decision support for data governance, the glossary, and data strategy.

• Support the development of EA principles and human-AI interaction principles and guidelines.

• Master and support the development of the WVI EA Method. Mentor or guide aspiring architects or other professionals.

KEY RESPONSIBILITES:

Enterprise strategy and roadmaps

• Own the enterprise Data and AI architecture strategy, principles, reference architecture and models, standards, and roadmap, aligned with business priorities, transformation outcomes, risk and investment constraints.

• Identify impacts, dependencies, and constraints across all other architecture domains (BDAT) and security.

• Assess the current data estate, identify capability gaps and technical debt, define target-state and transition architectures, and sequence modernization into achievable investment increments.

• Advise senior leadership on platform strategy, operating model, sourcing, build-versus-buy choices, vendor concentration, and the business implications of fragmented or under-governed data.

• Maintain decision records, capability maps, architecture debt registers, and measurable adoption plans so strategy remains connected to delivery.

• Maintain architecture lineage from vision and capabilities through to technologies.

Architecture practice and solution design

• Own the full Business, Data, Application, and Technology (BDAT) architecture for key strategic solutions, potentially working individually or with Solution Architects.

• Design end-to-end architectures for key strategic solutions.

• Design Architecture Building Blocks, Solution Building Blocks, and coordinate creation of associated patterns.

• Elicit business and functional requirements into implementable technical designs

• Ensure alignment with enterprise standards, principles, patterns, and target architectures

• Identify architectural risks, constraints, and trade-offs early

• Produce fit-for-purpose architecture artefacts (e.g. solution overviews, diagrams, ADRs) aligned with the TOGAF-based WVI EA/SA Methodology and ADS (Architecture Description Specification) for all BDAT (Business, Data, Application, Technology) domains and lifecycle phases.

• Maintain and develop baseline (as-is), transitional, and target architecture work products, and identify patterns or reusable Solution Building Blocks for the architecture content repository.

• Perform impact and root-cause analyses for changes and transitional architectures.

• Challenge and map business value quantified in the business case to functional requirements and candidates for solution architecture and design using value streams or other techniques

• Perform evaluations and cost breakdown analyses across BDAT domains, technology-stack layers, and project phases.

• Contribute to method development and share back to the architecture community.

• Contribute to RFP drafting and evaluation, and participate in vendor meetings.

• Lead architectural decision-making across a defined domain, platform, or portfolio scope

• Ensure solution architectures within scope align with enterprise standards, patterns, and approved target architectures

• Translate enterprise architectural guidance into clear, actionable design direction for Solution Architects and delivery teams

• Coordinate architectural decisions across related initiatives to ensure consistency and reuse

Data platform and integration architecture

• Design scalable, resilient, and secure architectures spanning ingestion, storage, transformation, processing, serving, and consumption across cloud, hybrid, and—where required—multi-cloud environments.

• Define fit-for-purpose patterns for batch and real-time pipelines, ETL/ELT, change data capture, APIs, event-driven integration, streaming, data sharing, and interoperability with operational systems.

• Define and develop MSA and event-driven architectures and patterns.

• Guide the evolution of data lake, data warehouse, lakehouse, semantic, knowledge, and analytical serving layers, including workload placement and separation of concerns.

• Set standards for availability, disaster recovery, scalability, performance, observability, data lifecycle, environment strategy, and platform cost optimization.

• Evaluate platform services and vendor solutions objectively; prevent avoidable lock-in through portable data, interfaces, metadata, and architecture contracts.

Information architecture, modeling, and data products

• Define enterprise information models, domain boundaries, canonical concepts, business vocabularies, taxonomies, ontologies, and semantic models that create consistent meaning across systems and use cases.

• Establish conceptual, logical, and physical modeling standards and guide implementation across relational, dimensional, document, graph, vector, and time-series data patterns.

• Shape business-aligned data products with clear ownership, contracts, quality expectations, access policies, lineage, service levels, and lifecycle management.

• Guide master and reference data strategy, authoritative sources, identity resolution, and cross-domain interoperability where shared entities require enterprise consistency.

• Ensure data is discoverable and reusable through metadata management, catalogs, lineage, glossaries, and governed self-service access.

• Define and apply data architecture patterns, principles, and reference designs across initiatives.

• Ensure solution architectures meet enterprise data standards, quality expectations, and governance requirements.

• Translate data strategy and policies into practical architectural guidance for delivery teams.

• Define and maintain logical and conceptual data models, data flow patterns, and integration approaches within portfolio or domain scope.

AI, machine learning, and knowledge architecture

• Architect AI-ready data foundations for analytics, machine learning, generative AI, semantic search, knowledge graphs, retrieval-augmented generation, and agentic solutions.

• Define patterns for data preparation, feature and embedding pipelines, chunking and indexing, vector retrieval, grounding, model gateways, orchestration, prompt and agent tooling, evaluation, and deployment.

• Specify data-to-AI contracts, including eligibility, classification, access propagation, freshness, provenance, quality, retention, auditability, and feedback-loop requirements.

• Establish model and AI service integration patterns that support routing, fallback, versioning, human oversight, observability, cost tracking, and safe experimentation.

• Differentiate and identify usecases for Statistics, ML, DL, NLP, NER or other advanced techniques.

• Partner with AI/ML engineering, data science, security, legal, privacy, and risk teams to turn responsible AI principles into enforceable technical controls.

Governance, security, privacy, and responsible AI

• Embed governance into platform and product design, covering ownership, stewardship, classification, retention, lineage, quality, access, consent, residency, and acceptable AI use.

• Assess architecture maturity and produce gap analyses for global, segment, or capability architectures.

• Define and enforce architecture controls for least privilege, role- and attribute-based access, encryption, secrets management, data loss prevention, segregation of duties, and audit logging.

• Ensure architectures comply with applicable regulatory, contractual, records-management, confidentiality, and information-security obligations.

• Design controls for AI-specific risks such as sensitive-data leakage, guardrails, prompt injection, insecure tool access, excessive agency, hallucination, provenance gaps, bias, and untraceable model decisions.

• Partner with data governance and quality leaders to convert policies into automated controls, monitoring, issue remediation, and accountable stewardship.

• Identify systemic data quality risks, architectural inconsistencies, and governance gaps across initiatives.

Architecture governance and delivery assurance

• Chair or contribute to architecture review boards and design forums, applying a proportionate, delivery-oriented governance process to major initiatives.

• Review and assure solution architectures, designs, data models, integration patterns, non-functional requirements, and vendor submissions for alignment with enterprise principles, approved target architectures, data standards, lineage requirements, and master data principles.

• Document architectural decisions, trade-offs, risks, exceptions, dependencies, and remediation actions, ensuring that deviations from approved standards are governed and time-bound.

• Create and maintain reference architectures, approved patterns, templates, guardrails, playbooks, and reusable building blocks that improve consistency and reduce repeated design effort.

• Guide Solution Architects and delivery teams in applying enterprise standards and patterns, providing clear, timely, and actionable architectural feedback throughout the delivery lifecycle.

• Prototype or technically validate high-risk architecture patterns and remain engaged through implementation, testing, migration, and production-readiness activities.

• Resolve architectural issues within delegated domain or portfolio authority and escalate cross-domain, enterprise-level, or high-impact matters to the Head of Enterprise Architecture.

• Track architecture adoption, outcomes, exceptions, technical debt, and emerging improvement opportunities, using delivery experience, regulatory developments, and technology evolution to continuously refine standards, methods, and roadmaps.

Leadership, influence, and capability building

• Serve as a trusted advisor to data, technology, security, risk, product, and business executives; communicate options and recommendations in terms of outcomes, cost, risk, and time to value.

• Build alignment across enterprise architecture, data engineering, analytics, AI/ML, application, integration, infrastructure, security, and governance teams.

• Raise design quality, and develop communities of practice that promote reusable patterns and shared accountability.

• Support portfolio planning, platform investment cases, vendor selection, delivery prioritization, and capability maturity assessments.

• Monitor relevant technology and regulatory developments, test emerging capabilities pragmatically, and introduce innovation when it has a clear enterprise use case.

• Work closely with Solution, Lead, and Enterprise Architects to embed data considerations early in design.

• Act as trusted data architecture advisor to stakeholders, including data owners, product teams, and governance forums.

KNOWLEDGE, SKILL AND EXPERIENCE:

Required Education, training, license, registration, and/or Certification

• Bachelor's degree in Information Technology, Computer Science, Business, Enterprise Architecture, or in a related discipline. Master’s or PhD Preferred. Additional education (e.g. MBA) or second degree in STEM field is an advantage.

• Enterprise Architecture certifications (e.g., TOGAF) desirable.

• The Open Group Certified Architect (Open CA) Certification (preferred).

Required Professional Experience

• Typically 10+ years of progressive experience across data architecture, data engineering, analytics platforms, enterprise architecture, or AI/ML architecture, including at least 5 years leading enterprise-scale architecture decisions.

• Demonstrated ownership of current-state, target-state, and transition architectures for complex, multi-system data environments.

• Strong practical knowledge of modern cloud data platforms, lakehouse/warehouse patterns, data integration, serverless, MSA integration , event driven architectures, integration modernization,  APIs, streaming, orchestration, metadata, lineage, data quality, and master/reference data.

• Experience designing AI/ML or generative AI foundations, including production data pipelines, retrieval patterns, evaluation, deployment, observability, and governance controls using cloud-native and cloud-agnostic technologies.

• Cloud experience in AWS/ Azure .

• Deep understanding of security, privacy, resilience, regulatory compliance, and responsible AI requirements in enterprise environments.

• Ability to elicit and translate business capabilities and non-functional requirements into architecture decisions, standards, roadmaps, and executable delivery guidance.

• Evidence of influencing senior stakeholders, facilitating cross-functional decisions, mentoring technical teams, and resolving ambiguity without relying on formal authority.

Required Language(s)

• Excellent command of spoken and written English, with the ability to communicate clearly and effectively with diverse stakeholders.

Preferred Experience, Knowledge and/or other Qualifications

• Architecture experience in a regulated or data-sensitive industry such as non-profit, financial services, insurance, healthcare, pharmaceuticals, legal/professional services, public sector, or telecommunications.

• Experience building or materially modernizing an enterprise data and AI capability from the ground up, including operating model and governance adoption not only technical platform delivery.

• Hands-on experience validating architecture through prototypes, reference implementations, design spikes, or production delivery leadership.

• Experience with knowledge graphs, semantic technologies, agentic AI, data mesh/product operating models, FinOps, or AI observability.

• Consulting or executive-advisory experience, including presentation of investment options, risk, and value to C-level audiences.

Applicant Types Accepted: