Summary

We are seeking an experienced Enterprise Architect (EA Generalist) to drive enterprise-wide architecture alignment, governance, and standards of adoption across business, application, infrastructure, data, and AI domains.

This role serves as a central architecture integrator and decision authority, ensuring that technology solutions including data platforms, AI/ML capabilities, and digital services align with enterprise standards, business capabilities, and regulatory requirements (including CMMC / NIST 800‑171).

The Enterprise Architect will operate across domains, supporting the Architecture Review Board (ARB), defining and evolving standards and reference architectures, and ensuring that architecture decisions deliver measurable business value, data-driven insights, and responsible AI adoption.

Enterprise Architecture Governance

• Serve as a core member of the Architecture Review Board (ARB)
• Review and govern architecture submissions to ensure Alignment with EA policies, standards, and reference architectures
• Compliance with regulatory requirements (e.g., CMMC, data protection)
• Appropriate use of data and AI capabilities
• Drive clear architecture decisions  Approved, conditional, deferred, or risk‑accepted
• Ensure traceability from architecture decisions → data → AI models → implementation

Standards & Reference Architecture Development

Key Responsibilities

• Define and maintain enterprise standards across Application, Infrastructure, Security, Data, and AI
• Develop and evolve reference architectures for Cloud platforms (Azure Gov, landing zones)
• Secure enclaves
• Data platforms (lakehouse, distributed data, unstructured data)
• AI/ML integration patterns (model deployment, inference, pipelines)
• Establish standards for:Data governance, classification, and lifecycle
• Responsible AI and model oversight
• Identify gaps through ARB trends and drive continuous improvement

Cross-Domain Architecture Integration

• Act as a generalist across all architecture domains, ensuring Applications are designed to leverage data and analytics effectively
• Infrastructure supports scalable AI and data workloads
• Security is embedded across data pipelines and AI models
• Bridge collaboration between Application Architecture
• Infrastructure / Cloud Engineering
• Security / AppSec Data Engineering / Data Architecture / AI teams

Data & AI Architecture Oversight

• Define and govern how data is structured, accessed, and used across the enterprise
• Ensure Data is trusted, governed, and properly classified
• Clear ownership and stewardship of data domains
• Govern AI and analytics solutions by ensuring proper model lifecycle management
• Validating data sources and quality feeding AI models
• Enforcing responsible AI principles (bias, explainability, auditability)
• Align AI initiatives to business capabilities
• Measurable value outcomes (revenue, efficiency, decision quality)

Business Alignment & Value Realization

• Map technology decisions to Business capabilities and value streams Data-driven decision making and AI enablement
• Ensure architecture supports Revenue growth
• Operational efficiency
• Data monetization opportunities
• Risk reduction
• Provide executive-level insights linking: Architecture → Data → AI → Business outcomes

Architecture Repository & Insight Management

• Maintain and leverage the EA repository (Ardoq, etc.)
• Ensure mapping across Applications ↔ Capabilities ↔ Data ↔ AI models ↔ Infrastructure
• Enable insights such as application and data rationalization
• Redundant data sources and silos
• AI opportunity identification
• Technical and data debt

Risk, Compliance & Security Alignment

• Ensure architecture designs support CMMC Level 2 / NIST 800‑171 controls
• Data protection and privacy requirements
• Secure enclave architecture principles
• Identify risks across Applications
• Data pipelines
• AI models
• Support audit readiness through Documented architecture decisions
• Data and AI traceability

Continuous Improvement & Enablement

• Analyze ARB trends to identify recurring gaps in application, data, or AI design
• Mature EA capabilities in Data governance
• AI architecture standards
• Enable teams by providing clear guidance on data and AI integration patterns
• Defining “what good looks like” for modern architectures
• Position EA as a strategic enabler of digital and AI transformation

Required Skills

• 7+ years in Enterprise Architecture or multi-domain solution architecture
• Strong experience across Application, Infrastructure, Data, and Security architectures
• Working knowledge of data platforms (data lakes, lakehouse, distributed data systems)
• AI/ML architecture concepts
• Experience with architecture governance (ARB, standards, policies)
• Ability to translate complex technical and data concepts into business value

Preferred Qualifications

• Familiarity with Azure (including Azure Gov)
• Modern data platforms and AI services
• Experience with EA tools (LeanIX, Ardoq, etc.)
• Exposure toAI/ML lifecycle management and governance
• Unstructured data architecture and data labeling

Success Measures

• Strong alignment across applications, data, AI, and infrastructure architectures
• Increased adoption of data-driven and AI-enabled architecture patterns
• Reduced data silos and improved data governance maturity
• Consistent adherence to EA standards and ARB decisions
• Demonstrated business value from data and AI initiative