Job Summary 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.
Essential Duties
- Solution Architecture & Technical Leadership
- 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.
- Data Platform & Snowflake Expertise
- 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 OneLake, lakehouses, 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.
- Data-Driven AI Architecture
- 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.
- Data Products & Lakehouse Architecture
- 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 products are treated as first-class engineering artifacts.
- Design and guide the implementation of data pipelines supporting batch, micro-batch, and streaming ingestion patterns based on business requirements.
- Architect master data management (MDM) and reference data strategies that ensure consistency across enterprise systems.
- Collaboration & Cross-Functional Engagement
- Partner closely with product, infrastructure, and application development teams within IT to ensure data and AI architectures are integrated seamlessly with enterprise systems.
- Engage directly with business stakeholders to deeply understand business problems before proposing technology solutions — consistently prioritizing business outcomes over technology preferences.
- Translate complex business and operational requirements from construction, facilities management, and building services into actionable, pragmatic architecture designs.
- Serve as a technical bridge between the Data & Analytics team and other IT disciplines — including Product, cybersecurity, cloud infrastructure, and Application Development — to ensure solution cohesion.
- Actively contribute to and influence TDIndustries’ enterprise technology strategy, roadmap, and governance forums.
- Team & Practice Development
- Provide technical mentorship and architecture guidance to data engineers, analytics engineers, and AI/ML engineers within the team.
- Establish and maintain architecture documentation, decision logs (ADRs), and reference architectures that build institutional knowledge and enable team independence.
- Conduct architecture and design reviews, offering constructive, evidence-based feedback that elevates team capability.
- Contribute to hiring and technical assessment of engineering talent, helping build a high-caliber data and AI team.
- Stay current with industry trends in data engineering, analytics, AI, and platform technology — proactively bringing relevant insights back to the team and influencing the technology roadmap.
- Perform other duties as required.
Minimum Requirements
- 15+ years of progressive experience in data engineering, data architecture, analytics, or closely related technical disciplines.
- 5+ years of hands-on, in-depth Snowflake architecture and engineering experience — this is a firm requirement. Candidates without this will not be considered.
- Demonstrated experience architecting and delivering production-grade data platforms on Microsoft Fabric or its predecessor Azure Synapse Analytics.
- Proven track record of designing and delivering data lakehouse architectures and data products at enterprise scale.
- Experience architecting AI/ML data infrastructure and working alongside data science or ML engineering teams.
- TOGAF certification or equivalent demonstrated enterprise architecture framework experience preferred.
- Experience in construction, building services, facilities management, or the built environment industry is a significant advantage — understanding of field operations data, project costing, IoT/BAS data is directly relevant.
- Demonstrated history of challenging architectural status quo and driving measurable improvements in data platform quality, performance, or cost efficiency.
- Business-first mindset — consistently starts with the business problem before selecting technology; avoids solutions in search of problems.
- Architectural depth and breadth — equally comfortable designing at the conceptual, logical, and physical architecture layers.
- Strong technical communication — able to articulate complex architectural decisions to engineering teams, product managers, and executive stakeholders with equal clarity.
- Collaborative leader — works with and through others across product, infrastructure, and app dev teams; not a siloed or ivory-tower architect.
- Constructive challenger — questions assumptions respectfully, backs positions with evidence, and changes course when presented with better information.
- Pragmatic problem-solver — balances architectural ideals with delivery realities; knows when to make pragmatic trade-offs without accumulating crippling technical debt.
- Continuous learner — actively tracks developments in data engineering, AI, and cloud platform ecosystems and brings relevant insights to the team.
- Core Platform (Required — In-Depth Proficiency)
- Snowflake: Architecture, advanced data modeling (Kimball/Data Vault), Snowpark, dynamic tables, data sharing, Snowflake Marketplace, cost optimization, performance tuning, and security configuration.
- Microsoft Fabric: OneLake, Fabric Lakehouse, Data Warehouse, Dataflows Gen2, Data Factory pipelines, Eventstream, Real-Time Intelligence, and Fabric notebooks.
- Power BI: Semantic model design, DAX optimization, enterprise-scale deployment via Fabric capacities, row-level security (RLS), and self-service BI governance.
- Architecture Frameworks & Patterns
- Lakehouse architecture patterns — medallion architecture, domain-oriented data products, data contracts.
- Cloud architecture on Microsoft Azure — including Azure networking, security, identity, and cost management as they relate to data platform deployments.
- Streaming and event-driven architectures — Kafka, Azure Event Hubs, Fabric Eventstream, or equivalent.
- AI & Analytics Architecture
- Vector databases, embedding pipelines, RAG architectures, and LLM integration patterns for data-driven AI applications. MLOps tooling and architectures — model serving, monitoring, feature stores, and experiment tracking.
- Semantic layer and metrics layer tooling (e.g., dbt Semantic Layer, Power BI semantic models).
- Advanced analytics architecture supporting predictive modeling, forecasting, and operational AI use cases.
- Data Engineering & Integration
- Modern ELT/ETL pipeline design — dbt, Azure Data Factory, Fabric Data Factory, or equivalent.
- API-based data integration and event-driven data ingestion patterns across enterprise systems (ERP, CRM, IoT, field operations platforms).
- Data quality frameworks, observability tooling (e.g., Monte Carlo, Great Expectations, or similar).
- Metadata management, data cataloging, and lineage tooling (e.g., Microsoft Purview or equivalent).
