Senior Data Architect (Semantic Modeling & Analytics)
About the Position
We are seeking a Senior Data Architect with Semantic Modeling to lead the evolution of a large-scale Power BI and SQL Server analytics environment while defining the organization's future-state analytics architecture. The role combines deep expertise in semantic modeling, Power BI architecture, analytical performance optimization, and enterprise data architecture. The architect will help maximize the value of the existing reporting platform, support emerging AI-driven analytics initiatives, and establish a roadmap toward a scalable, governed, and AI-ready analytics foundation. This is a highly strategic role requiring the ability to assess current-state analytical platforms, identify optimization opportunities, guide architectural decisions, and define long-term modernization strategies aligned with business objectives.
Responsibilities
- Assess and optimize enterprise-scale Power BI semantic models and reporting architectures. Assess and optimize enterprise-scale Power BI semantic models and reporting architectures.
- Design and improve aggregation, caching, and DirectQuery strategies for large analytical workloads.
- Review and optimize SQL Server-based analytical architectures supporting Power BI.
- Establish best practices for semantic modeling, dimensional design, governance, and performance.
- Define architecture patterns supporting AI-driven analytics, conversational BI, and semantic-layer consumption by AI Agents.
- Develop target-state architectures and phased modernization roadmaps balancing short-term value with long-term scalability.
- Guide engineering teams on semantic model design, analytical architecture, and platform governance.
- Lead architecture reviews, stakeholder workshops, and strategic technical discussions.
Requirements
- 10+ years in Data Architecture, Analytics Architecture, or Enterprise BI.
- Deep expertise in Power BI architecture, semantic modeling, and dimensional modeling.
- Strong knowledge of data warehousing principles and enterprise analytical data models.
- Proven ability to design and optimize enterprise-scale reporting and self-service analytics platforms.
- Advanced SQL and analytical data modeling skills.
- Strong understanding of DirectQuery, aggregations, caching strategies, and semantic model performance optimization.
- Knowledge of governance, security, lineage, and metadata management.
- Ability to lead architecture discussions with business and technical stakeholders.
Nice to Have
- Azure Data Platform services.
- AI-ready analytics architecture.
- Azure AI Foundry, Copilot, or Agentic AI initiatives.
- Enterprise data governance frameworks.
- TOGAF or equivalent architecture methodologies.
- Financial services, insurance, healthcare, or highly regulated industry experience.
- Experience with Microsoft Fabric architecture and workloads.
