Sr. Solution Architect - Data, Analytics & AI

The Sr. Solution Architect, Data, Analytics & AI is a critical, hands-on technical leadership role responsible for designing and driving the end-to-end solution architecture across TDIndustries’ data, analytics, and AI ecosystem. This is not a purely advisory role — the incumbent is expected to be deeply engaged in architecture design, technical decision-making, and solution delivery alongside engineering teams.

The role serves as the technical authority for the Technology Business Unit’s data platform built on Microsoft Fabric, Snowflake, and Power BI, while shaping the architecture for emerging data-driven AI capabilities. The ideal candidate leads with business outcomes first, challenges conventional thinking, and brings depth in data lakehouse architecture and data product engineering. Experience in or strong familiarity with the construction, building services, or facilities management industries is a meaningful advantage.

• Design and own the end-to-end solution architecture for TDIndustries’ data, analytics, and AI ecosystem, covering data ingestion, storage, transformation, semantic modeling, and consumption layers.
• Define and enforce architectural standards, patterns, and best practices across Microsoft Fabric, Snowflake, Power BI, and emerging AI platforms.
• Architect and guide the build-out of data products on a data lakehouse architecture, ensuring scalability, performance, governance, and reusability.
• Evaluate and recommend new technologies and platforms, including data-driven AI capabilities currently under evaluation for procurement, ensuring architecture is future-ready.
• Provide architecture governance — review and approve solution designs proposed by engineering, analytics, and AI teams, driving consistency and technical quality.
• Apply enterprise architecture principles, including TOGAF frameworks where applicable, to ensure structured, traceable architectural decision-making.
• Challenge the status quo — question inherited designs, call out technical debt, and drive architectural improvements without compromising delivery velocity.

• Serve as the primary deep-subject-matter expert for Snowflake, leading architecture decisions on data modeling, performance optimization, cost management, Snowpark, dynamic tables, and data sharing.
• Architect and oversee the integration between Snowflake and Microsoft Fabric, defining clear boundaries of responsibility across the two platforms and optimizing workload placement.
• Design data lakehouse patterns leveraging Microsoft Fabric’s One Lake, lake houses, warehouses, and data pipelines in coordination with Snowflake as the enterprise analytics warehouse.
• Define and enforce medallion architecture (Bronze / Silver / Gold) patterns across the data platform, ensuring data products are reliable, discoverable, and fit-for-purpose.
• Oversee Power BI semantic layer design, ensuring alignment with the data platform architecture and enabling governed self-service analytics.
• Architect the foundational data infrastructure required for AI and ML workloads — including feature engineering pipelines, vector stores, embedding strategies, and retrieval-augmented generation (RAG) architectures.
• Evaluate and define the architecture for AI platforms and tooling under procurement, ensuring alignment with the enterprise data platform and security standards.
• Define MLOps architecture patterns covering model training, deployment, monitoring, and lifecycle management within the TD environment.
• Collaborate with data science and ML engineering teams to translate AI/ML requirements into concrete, implementable platform and data architectures.
• Ensure AI solutions are built on a foundation of trusted, governed data — architecting data pipelines and quality controls that feed AI systems reliably.

• Lead the architecture and build-out of reusable, domain-oriented data products across Construction, Facilities, and Building Services to access trusted, curated data.
• Define data product specifications including schemas, SLAs, ownership, lineage, and consumption interfaces — ensuring data…