{"id":924825,"url":"https://alion.io/job/world-vision-enterprise-data-ai-integration-architect","title":"Enterprise Data & AI Integration Architect","company":{"id":6408,"name":"World Vision","domain":"worldvision.org","url":"https://alion.io/company/worldvision","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"B","score":83,"open_postings":54,"ghost_share":0,"stale_share":0.833,"repost_share":0.056,"time_to_fill_p50_days":13,"computed_at":"2026-11-02T06:13:15Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"staff","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Manila, Philippines","Dubai, United Arab Emirates","Amman, Jordan","São Paulo, Brazil","Santo Domingo, Dominican Republic","La Paz, Bolivia","El Salvador","Ecuador","New Zealand","Australia","Vietnam","Cyprus","United Kingdom","Cambodia","Indonesia","Bosnia and Herzegovina","Malaysia","Lebanon","Honduras","Romania","Armenia","Sri Lanka","Georgia"],"countries":["PH","AE","JO","BR","DO","BO","SV","EC","NZ","AU","VN","CY","GB","KH","ID","BA","MY","LB","HN","RO","AM","LK","GE"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":24000,"max_usd":55000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":1279},"experience_years_min":10,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"AI Agents","optional":false},{"name":"ETL/ELT","optional":false},{"name":"Hallucination","optional":false},{"name":"Knowledge Graph","optional":false},{"name":"Least Privilege","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"Machine Learning","optional":false},{"name":"NER","optional":false},{"name":"NLP","optional":false},{"name":"RAG","optional":false},{"name":"Semantic Search","optional":false},{"name":"Time Series","optional":false},{"name":"AWS","optional":true},{"name":"Azure","optional":true},{"name":"FinOps","optional":true}],"status":"live","first_seen_at":"2026-09-15T05:43:49Z","employer_posted_date":"2026-09-29","last_verified_at":"2026-11-02T22:30:44Z","board_verified":true,"closed_at":null,"days_open":48,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":48},"description":"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.\nCome join our 31,000+ staff working in nearly 100 countries and share the joy of transforming vulnerable children’s life stories!\nEmployee Contract Type:\nLocal - Fixed Term Employee (Fixed Term)Job Description:\nIMPORTANT INFORMATION:\nAll CVs should be submitted in English.\n\nThis position is open to candidates based in countries where World Vision International is legally registered to operate.\n\nJOB PURPOSE :\nThe 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.\nActing 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.\nThe 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.\nWithin 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.\nRole mandate:\nCreate 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.\n\nModernize the data landscape so governed, high-quality data can support operational use cases, analytics, machine learning, generative AI, and agentic workflows.\n\nReduce fragmentation, duplicated platforms, tech debt, inconsistent definitions, uncontrolled data movement, and architecture debt in partnership with data governance.\n\nEmbed security, privacy, resilience, regulatory compliance, responsible AI, observability, and cost management into architecture by design.\n\nIncrease delivery speed through reference architectures, approved patterns, data products, reusable components, and clear engineering standards.\n\nTranslate complex architectural trade-offs into decisions that executives, business leaders, risk teams, product owners, and engineers can act on.\n\nEnsure decision support for data governance, the glossary, and data strategy.\n\nSupport the development of EA principles and human-AI interaction principles and guidelines.\n\nMaster and support the development of the WVI EA Method. Mentor or guide aspiring architects or other professionals.\n\nKEY RESPONSIBILITES:\nEnterprise strategy and roadmaps\nOwn 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.\n\nIdentify impacts, dependencies, and constraints across all other architecture domains (BDAT) and security.\n\nAssess the current data estate, identify capability gaps and technical debt, define target-state and transition architectures, and sequence modernization into achievable investment increments.\n\nAdvise senior leadership on platform strategy, operating model, sourcing, build-versus-buy choices, vendor concentration, and the business implications of fragmented or under-governed data.\n\nMaintain decision records, capability maps, architecture debt registers, and measurable adoption plans so strategy remains connected to delivery.\n\nMaintain architecture lineage from vision and capabilities through to technologies.\n\nArchitecture practice and solution design\nOwn the full Business, Data, Application, and Technology (BDAT) architecture for key strategic solutions, potentially working individually or with Solution Architects.\n\nDesign end-to-end architectures for key strategic solutions.\n\nDesign Architecture Building Blocks, Solution Building Blocks, and coordinate creation of associated patterns.\n\nElicit business and functional requirements into implementable technical designs\n\nEnsure alignment with enterprise standards, principles, patterns, and target architectures\n\nIdentify architectural risks, constraints, and trade-offs early\n\nProduce 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.\n\nMaintain and develop baseline (as-is), transitional, and target architecture work products, and identify patterns or reusable Solution Building Blocks for the architecture content repository.\n\nPerform impact and root-cause analyses for changes and transitional architectures.\n\nChallenge 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\n\nPerform evaluations and cost breakdown analyses across BDAT domains, technology-stack layers, and project phases.\n\nContribute to method development and share back to the architecture community.\n\nContribute to RFP drafting and evaluation, and participate in vendor meetings.\n\nLead architectural decision-making across a defined domain, platform, or portfolio scope\n\nEnsure solution architectures within scope align with enterprise standards, patterns, and approved target architectures\n\nTranslate enterprise architectural guidance into clear, actionable design direction for Solution Architects and delivery teams\n\nCoordinate architectural decisions across related initiatives to ensure consistency and reuse\n\nData platform and integration architecture\nDesign scalable, resilient, and secure architectures spanning ingestion, storage, transformation, processing, serving, and consumption across cloud, hybrid, and-where required-multi-cloud environments.\n\nDefine 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.\n\nDefine and develop MSA and event-driven architectures and patterns.\n\nGuide the evolution of data lake, data warehouse, lakehouse, semantic, knowledge, and analytical serving layers, including workload placement and separation of concerns.\n\nSet standards for availability, disaster recovery, scalability, performance, observability, data lifecycle, environment strategy, and platform cost optimization.\n\nEvaluate platform services and vendor solutions objectively; prevent avoidable lock-in through portable data, interfaces, metadata, and architecture contracts.\n\nInformation architecture, modeling, and data products\nDefine enterprise information models, domain boundaries, canonical concepts, business vocabularies, taxonomies, ontologies, and semantic models that create consistent meaning across systems and use cases.\n\nEstablish conceptual, logical, and physical modeling standards and guide implementation across relational, dimensional, document, graph, vector, and time-series data patterns.\n\nShape business-aligned data products with clear ownership, contracts, quality expectations, access policies, lineage, service levels, and lifecycle management.\n\nGuide master and reference data strategy, authoritative sources, identity resolution, and cross-domain interoperability where shared entities require enterprise consistency.\n\nEnsure data is discoverable and reusable through metadata management, catalogs, lineage, glossaries, and governed self-service access.\n\nDefine and apply data architecture patterns, principles, and reference designs across initiatives.\n\nEnsure solution architectures meet enterprise data standards, quality expectations, and governance requirements.\n\nTranslate data strategy and policies into practical architectural guidance for delivery teams.\n\nDefine and maintain logical and conceptual data models, data flow patterns, and integration approaches within portfolio or domain scope.\n\nAI, machine learning, and knowledge architecture\nArchitect AI-ready data foundations for analytics, machine learning, generative AI, semantic search, knowledge graphs, retrieval-augmented generation, and agentic solutions.\n\nDefine patterns for data preparation, feature and embedding pipelines, chunking and indexing, vector retrieval, grounding, model gateways, orchestration, prompt and agent tooling, evaluation, and deployment.\n\nSpecify data-to-AI contracts, including eligibility, classification, access propagation, freshness, provenance, quality, retention, auditability, and feedback-loop requirements.\n\nEstablish model and AI service integration patterns that support routing, fallback, versioning, human oversight, observability, cost tracking, and safe experimentation.\n\nDifferentiate and identify usecases for Statistics, ML, DL, NLP, NER or other advanced techniques.\n\nPartner with AI/ML engineering, data science, security, legal, privacy, and risk teams to turn responsible AI principles into enforceable technical controls.\n\nGovernance, security, privacy, and responsible AI\nEmbed governance into platform and product design, covering ownership, stewardship, classification, retention, lineage, quality, access, consent, residency, and acceptable AI use.\n\nAssess architecture maturity and produce gap analyses for global, segment, or capability architectures.\n\nDefine and enforce architecture controls for least privilege, role- and attribute-based access, encryption, secrets management, data loss prevention, segregation of duties, and audit logging.\n\nEnsure architectures comply with applicable regulatory, contractual, records-management, confidentiality, and information-security obligations.\n\nDesign 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.\n\nPartner with data governance and quality leaders to convert policies into automated controls, monitoring, issue remediation, and accountable stewardship.\n\nIdentify systemic data quality risks, architectural inconsistencies, and governance gaps across initiatives.\n\nArchitecture governance and delivery assurance\nChair or contribute to architecture review boards and design forums, applying a proportionate, delivery-oriented governance process to major initiatives.\n\nReview 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.\n\nDocument architectural decisions, trade-offs, risks, exceptions, dependencies, and remediation actions, ensuring that deviations from approved standards are governed and...","description_format":"text","description_chars":17015,"description_truncated":true,"requirements":{"experience_years_min":10,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":false},"security_clearance":false,"languages":[{"language":"English","level":"All levels","optional":false}]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Nonprofits & Foundations","Public Health & 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