Enterprise Architect, Data

Wellington Management • Boston, MA

3d • Hybrid

About The Position

The Enterprise Data Architect is a senior role in Wellington Management's Enterprise Architecture practice, focused on shaping the firm's data architecture direction, data platform strategy, analytics architecture, data modeling standards, and AI-enabling data capabilities. The role develops enterprise strategies, roadmaps, reference architectures, patterns, standards, and technology guidance to enable trusted data, scalable analytics, responsible AI, reduced complexity, managed technology risk, and optimized cost.

The architect works with Technology Platform CIOs, the head of enterprise data, data product owners, solution architects, architecture domain working groups, data governance partners, and governance teams. The position balances strategic architecture leadership with practical solution engagement, emphasizing enterprise data direction, standards, reuse, governance, risk management, and long-term platform evolution. It requires translating complex data and AI architecture strategy into clear guidance for senior stakeholders and delivery teams, while balancing speed, cost, resiliency, security, data quality, responsible AI, and risk.

Wellington Management is an independent investment management firm serving institutional clients and mutual fund sponsors in more than 60 countries. The firm reports more than US$1 trillion in assets under management as of September 30, 2025. The position is based in a flexible work environment: employees work in offices four days a week and may work remotely one day a week.

Requirements

• Bachelor's degree in Technology, Computer Science, Data Science, Engineering, Information Systems, Business, or a related field.
• At least 10 years of experience in technology leadership, enterprise architecture, data architecture, solution architecture, platform architecture, analytics architecture, technology strategy, or large-scale technology transformation.
• Demonstrated experience developing data architecture strategies, roadmaps, reference architectures, modeling standards, data patterns, and governance materials in a complex enterprise environment.
• Strong understanding of modern data architecture, including data platforms and products, data modeling, domain-driven design, API and event-driven integration, semantic layers, business intelligence, analytics engineering, cloud-native architecture, infrastructure as code, DevSecOps, AI platforms, and reusable platform services.
• Experience assessing data and technology decisions for business value, trusted data, governance, risk, resiliency, security, cost, operational impact, responsible AI, and long-term maintainability.
• Proven ability to work effectively with senior technology leaders, product owners, solution architects, data engineers, analytics teams, risk partners, information security, infrastructure, data governance, privacy, and AI governance teams.
• Strong executive communication skills and ability to translate complex data, analytics, and AI architecture concepts into concise, business-aligned recommendations.
• Experience with architecture governance, architecture review boards, technology standards, non-functional requirements, data governance, technical debt management, and post-implementation validation.
• Ability to influence without relying solely on formal authority, building trust through expertise, collaboration, follow-through, and practical judgment.
• Ability to bring structure and clarity to ambiguous problems while maintaining momentum across multiple stakeholders and initiatives.

Nice To Haves

• Advanced degree or equivalent experience.
• Familiarity with architecture frameworks and modeling approaches, including TOGAF, Open Group standards, DAMA/DMBOK concepts, or related industry architecture and data management practices.

Responsibilities

• Develop and maintain data architecture strategies and roadmaps for assigned platforms, domains, and enterprise capabilities, aligned with technology, data, AI, and business priorities.
• Translate business outcomes and analytics objectives into data architecture direction, target-state designs, transition plans, and sequencing recommendations.
• Partner with platform leaders, product owners, analytics teams, and enterprise architects to create capability-based roadmaps prioritizing business value, governed data access, and trusted data reuse.
• Identify synergies, dependencies, constraints, and simplification opportunities across data platforms, analytical environments, integration patterns, reporting, and AI-enabling services.
• Provide strategic guidance for data platforms, data products, semantic layers, business intelligence, data modeling, responsible AI, agentic systems, metadata, lineage, and data governance.
• Communicate architecture tradeoffs, risks, cost implications, and decisions to senior leaders and delivery teams.
• Define and maintain enterprise data reference architectures, modeling standards, architecture patterns, principles, and reusable design guidance.
• Promote governed data products, strategic platforms, reusable data services, common semantic models, consistent integration patterns, and standard analytics architectures.
• Establish practical guidance for conceptual, logical, physical, dimensional, domain, and semantic data modeling.
• Work with data, security, infrastructure, developer experience, and AI working groups to create standards and patterns that support execution and data consistency.
• Contribute data strategies, standards, patterns, models, metadata, reference architectures, and governance decisions to the central architecture repository.
• Partner with platform leaders, analytics teams, CIOs, product owners, technology leads, and solution architects to shape data platform roadmaps and product architecture runways.
• Guide architecture for data, AI, analytics, business intelligence, API, event, semantic-layer, data-access, and cloud data platforms.
• Help teams connect business requirements to data standards, modeling patterns, platform capabilities, governance, and delivery execution.
• Advise teams on reuse, design simplification, and where custom data engineering may create competitive advantage.
• Support architects, engineers, and technical leads with solution design, non-functional requirements, integration patterns, cloud-native practices, data modeling, and architecture documentation.
• Identify opportunities to reduce duplication, retire redundant technologies, improve reuse and data quality, and align product-level decisions with enterprise architecture.
• Participate in the Architecture Review Board and related governance processes for significant data, analytics, and AI technology decisions.
• Assess architecture risks and gaps involving models, data quality, metadata, lineage, technology debt, resiliency, integration, privacy, and security.
• Help teams prepare architecture reviews by clarifying decisions, documenting tradeoffs, and grounding recommendations in business outcomes, governance expectations, and architecture principles.
• Support post-implementation architecture reviews and validations against approved direction and non-functional requirements.
• Partner with technology risk, information security, infrastructure, data governance, privacy, and AI governance teams on controls, compliance, resilience, responsible AI, and operational expectations.
• Lead or contribute to technology evaluations, proofs of concept, prototypes, and industry research that inform architecture and investment decisions.
• Assess emerging technologies and provide recommendations on build-versus-buy decisions, platform selection, analytics modernization, semantic layers, AI-enabling 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.
• Mentor architects, engineers, and technology leads; contribute to architecture training, certification, and practice development.
• Promote consistent architecture deliverables, templates, modeling practices, tools, metadata standards, and documentation practices.
• Support architecture domain working groups and foster collaboration across enterprise architecture, solution architecture, platform, analytics, product, and engineering teams.
• Build relationships with product owners, delivery teams, platform leaders, and business stakeholders; communicate recommendations clearly and facilitate workshops, design sessions, and technical planning discussions.
• Drive decisions through facts, analysis, and architectural expertise, and help teams navigate complex technical choices and competing priorities.

Benefits

• Retirement plan
• Health and wellbeing benefits
• Dental, vision, and pharmacy coverage
• Health savings account
• Flexible spending accounts
• Commuter program
• Employee assistance program
• Life and disability insurance
• Adoption assistance
• Back-up childcare
• Tuition/CFA reimbursement
• Paid time off, including leave of absence, paid holidays, volunteer, sick, and vacation time
• Salary and variable compensation; discretionary corporate bonus and/or incentives if eligible

What This Job Offers

• Job Type: Full-time
• Career Level: Senior