Director Enterprise Architecture

Director Enterprise Architecture

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 successfostering 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

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 "shadow IT/AI" 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.

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 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: