Director Enterprise Architecture
Description -
Role Description
The Director, Enterprise Architecture (EA) is a senior leadership role responsible for defining and governing the enterprise-wide technology architecture that enables business strategy, digital transformation, and AI-enabled innovation at scale. This role serves as the connective tissue between business strategy and technology execution, ensuring that architecture decisions accelerate simplification, operational efficiency, risk reduction, and long-term value creation.
The Director leads the Enterprise Architecture function as a strategic advisory capability, setting architectural vision, standards, and governance while partnering closely with executive leadership, business leaders, and technology teams across the enterprise.
Responsibilities
Enterprise Architecture Strategy & Vision
- Define and own the enterprise architecture vision, principles, and target-state roadmaps aligned to business strategy and long-term objectives.
- Translate an AI-forward and digital-first strategy into actionable architectural blueprints spanning business, data, application, integration, and technology domains.
- Ensure architecture decisions enable scalability, security, resilience, and cost efficiency across the enterprise.
Application & Technology Portfolio Optimization
- Drive enterprise-wide application portfolio rationalization using structured frameworks (e.g., TIME), reducing redundancy, technical debt, and operational complexity.
- Establish end-to-end application lifecycle governance to ensure new investments align with enterprise standards and strategic priorities.
- Guide build-versus-buy and platform decisions to maximize reuse, interoperability, and long-term return on investment.
AI, Data, and Integration Architecture Enablement
- Partner with data, platform, and security leaders to define reference architectures that enable scalable AI, analytics, and automation capabilities.
- Establish architectural patterns for integration, APIs, data platforms, and AI orchestration that support rapid innovation while maintaining enterprise-grade controls.
- Ensure AI solutions are designed with economic sustainability, governance, and risk management in mind.
Architecture Governance, Risk & Compliance
- Establish and lead enterprise architecture governance, including architecture review boards, standards, and decision frameworks.
- Embed security-by-design, data governance, and regulatory requirements directly into architecture standards to reduce risk and audit burden.
- Prevent fragmentation and 40shadow IT/AI51 by enabling compliant, self-service architectural patterns.
Leadership & Operating Model
- Lead and develop a high-performing Enterprise Architecture organization, including senior architects and architecture leaders.
- Evolve the EA function from a standards-focused role into a trusted strategic advisory capability.
- Promote modern ways of working, reusable patterns, and community-of-practice models across the technology organization.
Strategic Impact & Decision Authority
- Influences and shapes long-term technology and digital strategy across multiple business units and functions.
- Makes final decisions on enterprise architecture standards, frameworks, and major architectural direction.
- Owns architectural policies and governance mechanisms that directly impact business agility, cost structure, risk posture, and technology outcomes.
- Accountable for effective delivery of enterprise architecture objectives and measurable business outcomes.
Strategic Impact & Decision Authority
- Influences and shapes long-term technology and digital strategy across multiple business units and functions.
- Makes final decisions on enterprise architecture standards, frameworks, and major architectural direction.
- Owns architectural policies and governance mechanisms that directly impact business agility, cost structure, risk posture, and technology outcomes.
- Accountable for effective delivery of enterprise architecture objectives and measurable business outcomes.
Problem Solving & Innovation
- Addresses highly complex, ambiguous, and enterprise-wide challenges that span multiple domains, geographies, and platforms.
- Simplifies large-scale technology ecosystems to enable speed, resilience, and cost efficiency.
- Anticipates emerging technologies and industry trends, translating them into practical architectural strategies.
- Balances competing priorities across speed-to-market, cost, security, and scalability.
Scope of Accountability
- Enterprise-wide scope across business units, functions, and geographies.
- Ownership of enterprise architecture standards, roadmaps, and governance.
- Accountability for architecture-related planning, staffing, prioritization, and budget inputs.
- Decisions have long-term impact on business performance, technology investment, and operational risk.
Education
- Bachelor's degree in Computer Science, Information Systems, Engineering, or related technical discipline (required).
- Master's degree in Technology or Business Administration (strongly preferred);
Professional Experience
- 15+ years of progressive IT leadership; minimum 8 years in enterprise architecture at Fortune 500 or global enterprise scale.
- Demonstrated experience leading EA functions with 20+ architects across multi-geo, matrixed environments; accountability for $100M+ IT portfolios.
- Proven track record delivering enterprise AI adoption programs end-to-end: strategy, platform selection, implementation, scaling, and value realization.
- Hands-on experience architecting and deploying Generative AI and Agentic AI solutions in production enterprise environments.
- Experience driving AI-enabled productivity programs with quantifiable outcomes 40 cost reduction, cycle time compression, or revenue enablement.
- Track record of application rationalization, legacy modernization (ERP, mainframe), and cloud migration on a global scale.
- Background in digital value chain transformation (Lead-to-Order, Order-to-Cash, Acquire-to-Decommission) in a technology product company is highly desirable.
- Experience in M&A technology due diligence and integration architecture is a plus.
- Demonstrated C-suite and Board-level engagement on technology strategy and investment decisions.
Skills and experience
Knowledge
- TOGAF 9.2 / 10 40 Certified or Distinguished level, Zachman Framework Certification (preferred)
- SAFe (Scaled Agile Framework) Architect, ITIL 4 Managing Professional or Strategic Leader
- AWS Solutions Architect Professional, Azure Solutions Architect Expert
- Microsoft Certified: Azure AI Engineer Associate or Azure AI Fundamentals, AWS Certified Machine Learning 53 Specialty
- Enterprise AI governance programs: NIST AI RMF Practitioner, Responsible AI Institute certifications
Enterprise and AI architecture
- Expert-level command of TOGAF, Zachman, FEAF, and Gartner EA frameworks; Architecture Development Method (ADM) and EA governance models.
- Business Architecture: capability modeling, value stream mapping, and operating model design.
- Application Architecture: portfolio rationalization (Gartner TIME model), modernization patterns, LeanIX, and ServiceNow SPM.
- Data Architecture: data mesh, data fabric, MDM, enterprise data governance, and Lakehouse patterns (Snowflake, Databricks).
- Security Architecture: zero-trust, IAM, threat modeling, and privacy-by-design.
- Proficiency in architecture tooling: ArchiMate, BizzDesign, Lucidcharts, and executive-grade visual storytelling.
- Deep expertise designing enterprise AI reference architectures: model serving layers, LLM orchestration (LangChain, LlamaIndex, Semantic Kernel), vector databases (Pinecone, Weaviate, pgvector), and retrieval-augmented generation (RAG) pipelines.
- Agentic AI architecture: multi-agent orchestration frameworks (AutoGen, CrewAI), tool-use patterns, memory and context management, and human-in-the-loop design.
- AI platform architecture across leading enterprise stacks: OpenAI Frontier, Microsoft Azure OpenAI / Copilot Studio, Anthropic Claude, Salesforce Agentforce, ServiceNow AI Control Tower, and Google Vertex AI.
- MLOps and AI engineering: model lifecycle management, CI/CD for AI, feature stores, model registries, drift detection, and observability.
- Edge AI and on-device inference architectures - PC, print, and 3D manufacturing device portfolios.
- AI data architecture: synthetic data generation, data labeling pipelines, training data governance, and inference data privacy.
Enterprise AI Adoption & Value Realization
- Proven methodology for scaling AI from proof-of-concept to enterprise production: adoption playbooks, change management integration, and user enablement frameworks.
- AI business value modeling: ROI quantification, productivity gain measurement, cost-per-inference optimization, and AI TCO frameworks.
- Experience establishing AI Centers of Excellence (AI CoE): operating model design, talent strategy, toolchain standardization, and governance integration.
- AI FinOps: token economy management, GPU/compute cost allocation, model selection trade-off analysis (cost vs. latency vs. accuracy), and chargeback models.
- Practical knowledge of AI integration patterns: API-based inference, embedded AI in business workflows (ERP, CRM, ITSM), and AI-native application design.
- Experience measuring and communicating AI value realization to executive and board audiences through KPIs, dashboards, and business outcome narratives.
AI Governance, Risk & Responsible AI
- Comprehensive knowledge of NIST AI Risk Management Framework (AI RMF) and its operationalization within enterprise governance structures.
- Familiarity with EU AI Act requirements, AI liability frameworks, and global AI regulatory trends affecting enterprise technology deployment.
- Responsible AI principles in practice: bias detection and mitigation, model explainability (XAI), fairness metrics, and auditability by design.
- AI security architecture: prompt injection defense, adversarial ML, model poisoning prevention, and secure AI deployment patterns.
- Data privacy in AI: PII handling in training and inference, differential privacy techniques, and consent management for AI systems.
- AI audit and compliance: model cards, system cards, AI impact assessments, and third-party model risk evaluation.
Cloud, Infrastructure & FinOps
- Multi-cloud architecture expertise (AWS, Azure, GCP): cloud-native design, serverless, event-driven, and microservices patterns.
- AI-optimized infrastructure: GPU cluster design, inference optimization (quantization, distillation), and AI-forward network architecture.
- Containerization and orchestration: Kubernetes, Docker, and AI workload scheduling.
- FinOps / TBM: cloud cost governance, AI compute spend optimization, and IT spend transparency.
Integration, Data & Modernization
- API-first design and integration architecture: MuleSoft, Azure Integration Services, Boomi.
- Legacy modernization: SAP S/4HANA transformation, mainframe migration, application decommissioning.
- ITAM/SAM/CMDB and Acquire-to-Decommission (A2D) lifecycle governance.
- GRC platforms: ServiceNow GRC, RSA Archer; regulatory frameworks: SOX ITGC, GDPR, data residency.
Leadership & Executive Communication
- Executive presence: ability to influence C-suite and board on complex AI and technology strategy decisions.
- Team building: recruit, mentor, and scale globally distributed architecture teams across disciplines.
- Organizational change management for AI-driven transformation in large, matrixed enterprises.
- Thought leadership: publications, advisory council participation (e.g., HBR, Forbes Tech Council), and external industry representation.
- Strong OKR/KPI development linking architecture and AI investments to measurable business outcomes.
The pay range for this role is$190,950to$286,450USD annually with additional opportunities for pay in the form of bonus and/or equity (applies to United States of America candidates only). Pay varies by work location, job-related knowledge, skills, and experience.
Benefits:
HP offers a comprehensive benefits package for this position, including:
Health insurance
Dental insurance
Vision insurance
Long term/short term disability insurance
Employee assistance program
Flexible spending account
Life insurance
Generous time off policies, including;
4-12 weeks fully paid parental leave based
