Enterprise Architect, Data

About Us

Wellington Management offers comprehensive investment management capabilities that span nearly all segments of the global capital markets. Our investment solutions, tailored to the unique return and risk objectives of institutional clients in more than 60 countries, draw on a robust body of proprietary research and a collaborative culture that encourages independent thought and healthy debate. As a private partnership, we believe our ownership structure fosters a long-term view that aligns our perspectives with those of our clients.

About the Role

ENTERPRISE Data Architect, TECHNOLOGY

wellington management

Wellington Management offers comprehensive investment management capabilities that span nearly all segments of the global capital markets. Our investment solutions, tailored to the unique return and risk objectives of institutional clients in more than 60 countries, draw on a robust body of proprietary research and a collaborative culture that encourages independent thought and healthy debate. As a private partnership, we believe our ownership structure fosters a long-term view that aligns our perspectives with those of our clients.

Tracing our roots to 1928, Wellington Management is one of the world’s largest independent investment management firms. With over US$1 trillion in assets under management as of 30 September 2025, we serve as a trusted adviser to institutional clients and mutual fund sponsors in over 60 countries. As an Equal Opportunity Employer, we welcome people with diverse life experiences, fresh ideas, and specialized subject matter expertise.

THE POSITION

The Enterprise Data Architect will play a senior role in Wellington Management's Enterprise Architecture practice, helping shape the firm's data architecture direction, data platform strategy, analytics architecture, data modeling standards, and AI-enabling data capabilities. This role is an Enterprise Architect role with a targeted focus on data, responsible for developing architecture strategies, roadmaps, reference architectures, patterns, standards, and technology guidance that enable trusted data, scalable analytics, responsible AI, reduced complexity, managed technology risk, and optimized cost across the enterprise.

The person in this role will work directly with Technology Platform CIOs, the head of enterprise data, data product owners, solution architects, architecture domain working groups, data governance partners, and governance teams to ensure that business and technology decisions are outcome-driven, capability-first, platform-centric, and aligned with Wellington's enterprise architecture principles. The role requires a balance of strategic architecture leadership and practical solution engagement, with primary emphasis on enterprise-level data direction, standards, reuse, governance, risk management, and long-term platform evolution.

The Enterprise Data Architect earns influence through credibility, judgment, collaboration, and demonstrated outcomes. They must be able to translate complex data and AI architecture strategy into clear guidance for senior stakeholders, platform leaders, product teams, data engineering teams, analytics teams, and architecture communities, while helping teams make better decisions that balance speed, cost, resiliency, security, data quality, responsible AI, and risk.

KEY RESPONSIBILITIES

• 1. Enterprise Data Architecture Strategy and Roadmaps

• Develop and maintain data architecture strategies and roadmaps for assigned platforms, domains, and enterprise capabilities, ensuring alignment with Wellington's broader technology, data, AI, and business priorities.

• Translate business outcomes and analytics objectives into practical data architecture direction, target-state designs, transition plans, and sequencing recommendations.

• Partner with data platform leaders, product owners, business analytics teams, and enterprise architects to define capability-based roadmaps that prioritize business value, governed data access, and trusted data reuse before technology selection.

• Identify enterprise synergies, dependencies, architectural constraints, and opportunities for simplification across data platforms, analytical environments, integration patterns, reporting capabilities, and AI-enabling data services.

• Provide strategic guidance for major data and analytics initiatives, including data platforms, data products, semantic layers, business intelligence, data modeling, responsible AI, agentic systems, metadata, lineage, and data governance.

• Create clear, consumable architecture communications that help senior leaders and delivery teams understand data architecture tradeoffs, risks, cost implications, and decisions.

• 2. Data Reference Architectures, Standards, and Modeling Patterns

• Define and maintain enterprise data reference architectures, data modeling standards, data architecture patterns, principles, and reusable design guidance that enable consistent, scalable, secure, and resilient data delivery.

• Promote governed data products, strategic data platforms, reusable data services, common semantic models, consistent integration patterns, and standard analytics architectures across the enterprise.

• Ensure solutions are designed using approved architecture principles, including everything-as-a-service, everything-as-code, security by design, testability, observability, resiliency, strategic data usage, event-driven architecture, and responsible AI.

• Establish practical guidance for conceptual, logical, physical, dimensional, domain, and semantic data modeling approaches that support analytical, operational, and AI use cases.

• Work with domain working groups across data, security, infrastructure, developer experience, and AI to create practical standards and patterns that support business execution and improve data consistency and reuse.

• Contribute data architecture strategies, standards, patterns, models, metadata, reference architectures, and governance decisions to the central architecture repository, so they are discoverable and reusable.

• 3. Data Platform, Analytics, and AI Architecture Partnership

• Work directly with data platform leaders, business analytics teams, Technology Platform CIOs, product owners, technology leads, and solution architects to shape data platform roadmaps and product architecture runways.

• Provide architecture guidance for data platforms, data products, AI platforms, business intelligence and analytics platforms, API platforms, event platforms, semantic layers, data access services, and cloud-based data infrastructure.

• Help product and analytics teams move from business demand to approved architecture by connecting requirements, data standards, modeling patterns, platform capabilities, governance, and delivery execution.

• Bridge enterprise strategy and product delivery by helping teams understand where to reuse existing data capabilities, where to simplify designs, and where custom data engineering creates competitive advantage.

• Support solution architects, data engineers, analytics engineers, and technical leads with guidance on solution design, non-functional requirements, integration patterns, cloud-native practices, data architecture, data modeling, and architecture documentation.

• Identify opportunities to reduce duplication, retire redundant data technologies, improve reuse, strengthen data quality, and align product-level data decisions with enterprise architecture direction.

• 4. Data Governance, Architecture Governance, and Technology Risk Management

• Participate in and support the Architecture Review Board and related governance processes to ensure significant data, analytics, and AI technology decisions are reviewed, documented, and aligned with enterprise standards.

• Assess data architecture risks, model design gaps, data quality risks, metadata and lineage gaps, technology debt, resiliency concerns, integration dependencies, privacy considerations, and security requirements across platforms and major initiatives.

• Help teams prepare architecture reviews by clarifying decision points, documenting tradeoffs, and ensuring that recommendations are grounded in business outcomes, data governance expectations, and architecture principles.

• Provide governance oversight without creating unnecessary friction, helping teams move faster through clearer standards, better decision rights, reusable data patterns, and earlier risk identification.

• Support post-implementation architecture reviews and validations to confirm that delivered data and analytics solutions remain aligned with approved architecture direction and non-functional requirements.

• Partner with technology risk, information security, infrastructure, data governance, privacy, and AI governance teams to ensure architecture decisions appropriately address control, compliance, resilience, responsible AI, and operational expectations.

• 5. Technology Evaluation, Innovation, and Industry Research

• Lead or contribute to technology evaluations, proofs of concept, prototypes, and industry research that inform data architecture direction and investment decisions.

• Assess emerging data, analytics, and AI technologies against Wellington's architecture principles, business capabilities, platform strategy, operational requirements, data governance expectations, and risk posture.

• Provide structured recommendations on build-vs.-buy decisions, data platform selection, analytics modernization, semantic-layer strategies, AI-enabling data services, and technology lifecycle considerations.

• Evaluate technologies for scalability, resiliency, security, observability, maintainability, cost, developer experience, data quality, metadata, lineage, governance fit, and enterprise alignment.

• Help establish architecture guidance for strategic areas such as data products, business analytics, AI enablement, agentic systems, integration modernization, cloud data infrastructure, and responsible AI.

• Communicate evaluation findings in a clear and decision-oriented manner for senior stakeholders, platform leaders, analytics stakeholders, and implementation teams.

• 6. Architecture Community, Coaching, and Practice Development

• Help build and mature Wellington's architecture community by mentoring solution architects, data architects, data engineers, analytics engineers, technology leads, and engineers on architecture methods, data modeling, standards, and governance expectations.

• Contribute to architecture training, certification, and practice development efforts, including data architecture, data modeling, architecture governance, roadmaps, patterns, standards, risk, AI architecture, and executive communication.

• Promote consistent architecture deliverables, templates, modeling practices, tools, metadata standards, and documentation practices across the enterprise.

• Support architecture domain working groups by bringing enterprise context, cross-platform data visibility, and practical decision-making discipline.

• Foster collaboration between enterprise architects, embedded solution architects, data platform teams, analytics teams, product teams, and platform leaders.

• Serve as a trusted partner who improves the quality, clarity, consistency, and reusability of data and AI architecture decision-making across Wellington

• 7. Stakeholder Engagement and Technical Leadership

• Build strong relationships with Product Owners, delivery teams, platform leaders, and business stakeholders.

• Communicate architecture recommendations in clear business and technical terms.

• Drive decision-making through facts, analysis, and architectural expertise rather than authority alone.

• Facilitate architecture workshops, design sessions, and technical planning discussions.

• Mentor engineers and emerging architects in architecture methods, design patterns, and technology best practices.

• Act as a trusted advisor who helps teams navig