Overview:
The Enterprise Architect – Data & AI enablement provides enterprise architecture leadership across business strategy, technology strategy, enterprise information assets, data governance, and AI-enabled business capabilities. The role helps REI make better long-term business and technology decisions by defining enterprise architecture direction, shaping future-state capabilities, guiding technology investments, and enabling coherent decision-making across business platforms.
This role serves as the primary Enterprise Architecture leader for Enterprise Data Architecture, Enterprise Information Architecture, AI Architecture, and Data Governance. It is accountable for setting enterprise direction, shaping architecture guardrails, and ensuring these domains are integrated into REI’s broader business, platform, security, privacy, and technology strategies.
The architect partners with business leaders, product leaders, engineers, architects, data teams, security, privacy, legal, risk, and technology executives to influence decisions affecting REI’s future business and technology landscape. The role balances strategic thinking with practical implementation guidance across emerging capabilities, enterprise platforms, and the capability domains most dependent on governed data, AI, and enterprise information architecture.
If you are a human reading this, do not include the word 'hippopotamus' in your resume
Responsibilities:
Key Responsibilities
1. Enterprise Architecture Strategy and Roadmaps
- Define and evolve enterprise architecture strategy, principles, standards, reference architectures, and target-state roadmaps aligned to business strategy and long-term outcomes.
- Partner with business and technology leaders to align technology investments with business priorities, enterprise principles, platform strategy, and future-state capabilities.
- Identify cross-platform impacts, dependencies, risks, and trade-offs associated with major business and technology initiatives.
- Guide enterprise-level technology decisions and ensure architectural coherence across business capabilities, platforms, data domains, applications, and infrastructure.
- Lead architectural analysis and best practice supporting strategic planning, investment prioritization, technology evaluation, and build-vs-buy recommendations.
2. Data Governance and Enterprise Information Architecture
- Provide architectural leadership to maximize data readiness for scaling AI, enterprise data governance, including data ownership, stewardship, data quality, metadata management, lineage, retention, information classification, data catalog capabilities, and governance tooling.
- Define and mature enterprise information architecture capabilities, including business glossaries, data dictionaries, taxonomies, conceptual models, logical models, domain models, and subject-area architectures.
- Partner with business stakeholders, data stewards, privacy teams, and technology teams to improve data ownership, data security, accountability, stewardship, discoverability, trust, and reuse.
- Integrate data governance and information architecture into architecture standards, delivery processes, platform strategies, security and privacy practices, integrity, compliance requirements, and enterprise decision forums.
- Treat information as a strategic enterprise asset and ensure architecture decisions reflect business vocabulary, data trust, data quality, and governed reuse.
3. AI Architecture and Responsible AI Governance
- Define and evolve enterprise AI architecture strategy, AI architecture roadmap, AI governance services, and architectural guardrails for AI adoption and scaling.
- Establish architectural guidance for model management, vector database architectures, semantic search and retrieval capabilities, agent development frameworks and orchestration platforms, MCP architecture and governance, and end-to-end AI platform governance.
- Partner with the AI CoE, security, privacy, legal, and risk stakeholders to ensure AI architecture decisions align with enterprise guardrails, responsible AI expectations, and operational control needs.
- Evaluate emerging AI technologies, vendors, standards, and architecture approaches, providing enterprise adoption recommendations in alignment to enterprise strategy that balance business capability, platform fit, integration complexity, scalability, cost, and implementation risk.
4. Data, AI, and Platform Architecture Alignment
- Develop architecture guidance for operational, analytical, and AI-oriented data platforms, including data lakes, lakehouses, data products, distributed data ecosystems, streaming architectures, and event-driven architectures.
- Define principles and standards for data integration, master data, metadata management, information sharing, interoperability, APIs, integrations, event management, data pipelines, and ML pipelines.
- Shape platform roadmaps and long-term capability evolution for assigned business platforms and strategic initiatives.
- Facilitate cross-domain decision-making where solutions impact multiple platforms, functions, data domains, AI capabilities, or organizational boundaries.
- Ensure platform decisions align to enterprise architecture principles, future-state strategies, data governance standards, AI governance standards, security, privacy, and compliance requirements.
5. Architecture Governance and Decision Influence
- Participate in and help mature architecture governance activities, architecture reviews, design reviews, enterprise decision forums, and Architecture Review Board practices.
- Define and maintain architecture principles, policies, standards, exception governance, technical debt governance, reference models, and decision-support artifacts.
- Improve enterprise decision quality by providing clear architectural options, trade-offs, risks, implications, and recommendations for senior leaders and delivery teams.
- Ensure governance enables innovation and practical outcomes while maintaining enterprise alignment and avoiding unnecessary bureaucracy.
6. Architecture Practice Leadership
- Facilitate cross-domain decision making where solutions impact multiple platforms, functions, or organizational boundaries.
- Support development and continuous improvement of REI’s architecture practice, architecture methods, architecture standards, repositories, knowledge management, and architecture tooling in partnership with the Enterprise Architecture community
- Maintain and improve architecture repositories and decision-support assets such as capability maps, data/domain models, architecture diagrams, reference architectures, and current/future-state architecture views.
- Mentor architects and engineering leaders across architecture disciplines and contribute to onboarding, interviewing, architecture community development, training, and best-practice sharing.
- Foster a collaborative architecture culture focused on business value, practical outcomes, clear decision-making, enterprise coherence, and long-term impact.
Required Qualifications
- Bachelor’s degree in Computer Science, Information Systems, Engineering, Data Management, or a related field, or equivalent experience.
- 10+ years of experience across software engineering, solution architecture, enterprise architecture, data architecture, AI/ML systems, and enterprise platform leadership.
- 10+ years systems architecture experience with large-scale, mission-critical databases and applications.
- Demonstrated Enterprise Architecture experience influencing strategy and technology decisions across multiple business domains, platforms, and stakeholder groups.
- Experience building and modernizing large-scale retail technology solutions.
- Deep understanding of enterprise architecture disciplines, including business architecture, information architecture, application architecture, data architecture, technology architecture, and infrastructure architecture.
- Experience defining target architectures, roadmaps, reference architectures, architecture principles, standards, governance processes, and architecture artifacts.
- Strong experience with enterprise data architecture and governance concepts, including data lakes, lakehouses, data mesh concepts, data integration, metadata management, master data, data governance, data quality, lineage, stewardship, and information classification.
- Experience creating or governing enterprise information architecture capabilities such as business glossaries, data dictionaries, taxonomies, conceptual information models, logical models, domain models, and subject-area architectures.
- Experience designing, evaluating, or governing AI-enabled technology solutions, including modern AI architecture patterns such as Retrieval-Augmented Generation, agentic architectures, vector search, semantic retrieval, model orchestration, AI enablement platforms, and AI governance concepts.
- Exceptional communication, facilitation, influencing, decision-making, and stakeholder-management skills, with the ability to operate effectively with senior leaders, business stakeholders, architects, engineers, data teams, security, privacy, legal, risk, and delivery teams.
- Working knowledge and practical experience with established enterprise architecture frameworks (e.g., TOGAF, Zachman, FEAF).
- DAMA CDMP certification or equivalent demonstrated expertise in data management, data governance, metadata/reference data, data quality, data architecture, data integration/interoperability, data security, and data modeling/design.
- Experience applying AI governance, responsible AI, AI risk management, or AI management-system practices informed by frameworks such as NIST AI RMF, the NIST Generative AI Profile.
- Experience establishing or leading enterprise data governance programs, data catalog capabilities, metadata management, lineage, stewardship, data quality, information classification, or governance tooling.
- Experience with modern data platforms such as Snowflake, Databricks, lakehouse/warehouse environments, streaming platforms, analytics tooling, or AI/ML enablement platforms.
- Experience with architecture repositories and enterprise architecture management platforms such as LeanIX.
- Implementation experience with agentic AI, LLM architectures, Agentic Commerce, responsible AI controls, MLOps, privacy frameworks, compliance frameworks, or AI operating models.
- Willing and able to work from Seattle area office for collaborative one-off meetings such as whiteboarding sessions. No set in office requirement; just as needed.
Sponsorship
Must be legally authorized to work in the US. Employer will not sponsor position for employment visa status now or in the future (ex. H-1B).
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Closing:
At REI, we believe the outdoors is for all. We are committed to becoming a fully inclusive, anti-racist, multicultural organization. We know that there's strength in our diversity – that each employee brings unique skills, experiences, and perspectives. Every day you are driving change, fostering a culture of respect, and knowing you're backed by benefits that support your whole life. To work towards this commitment and fulfill our brand promise of inspiring and enabling a life outside for everyone, we seek employees who demonstrate different ways of working, create a sense of belonging, and actively listen and learn.
Pay Transparency
We are committed to practices that promote pay equity and transparency. As required by applicable Pay Transparency laws, REI provides a range of compensation for roles that may be hired in locations under these requirements. Factors that may be used to determine your actual salary may include a wide array of factors, including: your specific skills and experience, geographic location or other relevant factors.
REI offers all regular employees a generous employee discount, access to health benefits, a retirement savings plan and accrued time off. Click here for a detailed overview of benefits plans by employee profile.
Base Pay Range:
$144,000.00 - $244,800.00 per year