Senior Director, Enterprise Architecture

Foot Locker•,

10d•$200,000 - $265,000•Remote

About The Position

The Senior Director, Enterprise Architecture (EA) leads a modern, AI-driven and execution-oriented enterprise architecture capability. This role focuses on translating business and technology strategy into working guardrails, reusable platform patterns, and measurable outcomes. The emphasis is on hands-on, practical architecture that accelerates delivery, reduces risk and complexity, and embeds architectural thinking directly into product and engineering workflows. What Success Looks Like (Outcomes) Enterprise Architecture is embedded in delivery, not operating as an ivory-tower or documentation-only function. AI and platform decisions are standardized through enterprise reference architectures and paved roads. Architecture standards, guardrails, and decisions are enforced through automation and AI-enabled governance. Measurable improvements are realized in delivery rework reduction, modernization velocity, solution quality, cost optimization, and risk reduction.

Requirements

Minimum Bachelor’s degree in Computer Science, Engineering, Information Systems, or a related technical field, or equivalent practical experience. Master’s degree preferred.
10+ years of experience in enterprise, platform, solution, or domain architecture roles within large-scale, complex organizations.
Demonstrated leadership in AI-driven architecture, including enterprise AI platforms, generative AI, LLM-based systems, RAG, embeddings, prompt orchestration, or agent-based solutions.
Strong understanding of modern AI system architecture, including model lifecycle management, LLMOps/MLOps, knowledge architectures, and human-in-the-loop decision patterns.
Proven experience defining and operationalizing enterprise guardrails such as architecture standards, reference architectures, and automated governance.
Hands-on experience with cloud platforms, data ecosystems, and modern integration patterns (API-first, event-driven, domain-oriented).
Ability to translate AI and architecture tradeoffs into business outcomes including cost optimization, risk reduction, and delivery acceleration.
Strong executive presence with the ability to influence senior stakeholders without formal authority.
Build and mentor a high-performing architecture team, fostering innovation, collaboration, and alignment across the enterprise.
Ability to translate complex technical, architectural decisions and trade-offs effectively to technical and non-technical stakeholders, including executive leadership.
Collaborative, pragmatic, and delivery-oriented leadership style.
Strong mentoring, coaching, and architecture community-building mindset.

Responsibilities

Define and evolve the enterprise AI architecture across user experience, governance, agent orchestration, verification and safety, models and LLMOps, data and knowledge, and runtime layers.
Establish responsible AI, security, privacy, observability, and cost controls as architectural defaults across platforms.
Partner with engineering and product leadership to ensure AI solutions are scalable, reusable, governed, and enterprise-safe.
Drive adoption of executable EA practices where architecture is machine-readable, automatable, and enforceable.
Establish architecture-as-code and policy-as-code patterns using version-controlled standards, ADRs, and automated CI/CD checks.
Shift governance left into delivery pipelines, reducing reliance on manual review boards.
Work hands-on with product and engineering teams on priority initiatives.
Participate directly in roadmap planning, backlog refinement, solution design, and architectural tradeoff discussions.
Ensure modernization efforts leverage standard cloud-native, API-first, and event-driven patterns.
Own enterprise architecture standards delivered as practical assets: templates, reference implementations, and guardrails.
Operate architecture through an inner-source model that encourages contribution from architects and engineers.
Lead and mature the architecture community of practice to drive consistency, reuse, and continuous learning.
Establish architecture governance frameworks and ensure adherence to security, privacy, and regulatory standards such as SOC 2, GDPR, and HIPAA
Lead architecture governance forums and define principles, standards, and best practices to ensure consistency and compliance
Use architectural insight and AI-driven analysis to identify cost, risk, and simplification opportunities.
Establish transparency into application lifecycle, dependencies, and modernization readiness.
Translate architecture insights into tangible financial and operational outcomes.