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.