Domain Architect - AI/Domain Architect – Data, Analytics & AI || Remote

Key Responsibilities

Domain Architecture & Strategy

• Own and maintain the Data & Analytics architecture and technology roadmap for the assigned business domain.
• Develop a deep understanding of the domain's business processes, capabilities, data, applications, analytics needs, and strategic priorities.
• Translate business strategies and capabilities into target-state data, analytics, integration, and AI architectures.
• Establish current-state, transitional, and target-state architectures that guide modernization and investment decisions.
• Identify architectural gaps, redundancies, technical debt, and opportunities for simplification and modernization.
• Ensure individual initiatives contribute toward a cohesive enterprise and domain architecture rather than creating isolated solutions.

 

Data Architecture & Data Products

• Define and guide the development of domain data models, unified data models, and reusable data products.
• Partner with Data Architects and Data Modelers to review and challenge conceptual, logical, and physical data designs.
• Promote common definitions, reusable data structures, interoperability, and consistent modeling standards across the domain.
• Establish clear domain boundaries, ownership, authoritative data sources, and system-of-record/system-of-reference patterns.
• Drive modernization and decommissioning of redundant or legacy data structures where appropriate.
• Ensure data products are designed for reuse across reporting, analytics, operational use cases, Data Science, and AI.

Analytics, BI & AI Architecture

• Guide architecture for reporting, analytics, advanced analytics, machine learning, generative AI, and agentic AI use cases within the domain.
• Partner with BI and Analytics teams to promote reusable semantic and unified data models rather than report-specific data structures.
• Ensure analytical solutions leverage trusted, governed, and reusable enterprise data.
• Evaluate where AI capabilities can create measurable business value within the domain.
• Ensure data foundations are designed to support both traditional analytics and emerging AI workloads.
• Provide architectural guidance for AI integration, grounding, data access, governance, security, and observability.

Architecture Governance & Solution Reviews

• Serve as the architecture authority for Data & Analytics solutions within the assigned domain.
• Lead and participate in architecture reviews for major initiatives and analytical use cases.
• Review proposed architectures and challenge designs when they introduce unnecessary complexity, duplication, security risks, or technical debt.
• Ensure solutions comply with enterprise architecture principles, approved technology patterns, data standards, security requirements, and governance policies.
• Document architectural decisions, exceptions, risks, dependencies, and technical recommendations.
• Balance enterprise standards with practical business delivery needs and speed-to-value.

Data Governance, Quality & Security

• Embed Data Governance, Data Quality, Security, Privacy, Lineage, and Observability into architecture designs.
• Partner with Data Governance teams to establish domain ownership, stewardship, business definitions, metadata, classifications, and critical data elements.
• Ensure appropriate controls exist for sensitive and regulated information.
• Promote automated data quality monitoring for data both at rest and in transit.
• Ensure architectures provide traceability from source systems through transformation, data products, semantic models, analytics, and downstream consumption.
• Support enterprise security, risk, audit, and regulatory objectives through architecture standards and controls.

Modernization & Technical Debt

• Identify legacy platforms, pipelines, data models, reports, integrations, and technologies that should be modernized or retired.
• Develop architecture roadmaps for moving legacy capabilities toward strategic enterprise platforms and standards.
• Promote consolidation and reuse to reduce duplicate pipelines, datasets, reports, and technology capabilities.
• Partner with engineering and platform teams to establish practical migration and decommissioning strategies.
• Ensure modernization efforts improve scalability, reliability, maintainability, security, and cost efficiency—not simply move existing technical debt to a new platform.

Cross-Functional Leadership

• Act as the architectural bridge between business leadership and technology delivery teams.
• Partner closely with Enterprise Architects, Solution Architects, Data Architects, Security Architects, Data Engineers, Analytics Engineers, Data Scientists, BI Developers, Product Owners, and Data Governance professionals.
• Influence architecture decisions across teams without relying solely on direct organizational authority.
• Facilitate technical discussions and drive teams toward clear architecture decisions when competing approaches exist.
• Mentor architects, engineers, data modelers, and technical leads on architecture principles and modern Data & Analytics practices.
• Champion architectural thought leadership and introduce emerging technologies and patterns where they provide meaningful business value.