Director Enterprise Architecture
About HP
At HP, you’ll have a chance to create tools, technology, and solutions that reshape the way the world works in the future. If you’re looking to join a company that allows you to connect with a network of professionals eager to support you in doing your best work, we want to talk to you. Our legendary culture guides every employee toward success—fostering collaboration and driving innovation. Operating in over 170 countries, HP is always creating new services, products, and capabilities giving you more opportunities to advance your career. Here, innovation is the key to professional development and career mobility. When you join HP, you’re joining a company that believes every voice matters and that we all deserve a seat at the table. From the boardroom to factory floor, we create a culture where everyone is respected and where people can be themselves. You will be part of a global laboratory where different perspectives and experiences will help you solve problems in new ways. This is where you can build a long and wide-ranging career.
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
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.
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.
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.
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.
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.
Requirements
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 — 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 — 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 – 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
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
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 Arche