AI & Data Solutions Architect

Salary: USD
150000 - USD
180000 per year

• Direct Hire/Perm
• Pay: $150-180k + 10% bonus
• Must be eligible to work in the US without sponsorship now or in the future.

Summary:

We are seeking a technically fluent, AI-forward data engineer and solutions architect to build and own the next-generation data and AI platform that powers how our organization accesses, analyzes, and acts on commercial real estate data. This role is the builder behind our "talk to your data" initiative.

You will architect and implement Microsoft Fabric/One Lake as our centralized data platform, build governed ingestion pipelines from Yardi, SharePoint, CoStar, Green Street, Argus, and other CRE systems, and develop AI agents and natural language interfaces that enable business users to query the portfolio without writing a single line of code or opening a dashboard. You will work directly with IT, FP&A, and other stakeholders to define data requirements and ensure the platform delivers reliable, governed, AI-ready data.

The ideal candidate is a builder, comfortable in Python, SQL, and vector databases, experienced with enterprise cloud data platforms, and an evangelist for the intersection of AI and structured business data. CRE knowledge is a plus, not a prerequisite; the ability to learn a domain quickly and translate it into data architecture is required.

Primary Responsibilities:

• Architect and implement Microsoft Fabric/One Lake as our centralized, cloud-native data platform.
• Build governed ingestion pipelines from Yardi and Data Freedom, SharePoint, Argus, CoStar, Green Street, Measurabl, and other CRE data sources.
• Design and deploy AI agents and natural language interfaces using the Microsoft Copilot and Azure AI stack governed within our Microsoft 365 tenant.
• Implement schema validation, business rule enforcement, and data quality monitoring at the ingestion layer.
• Serve as the platform engineering partner to analysts, define and maintain the data foundation that BI reporting consumes.
• Champion AI and emerging technology adoption within enterprise-governed, security-first boundaries.
• Maintain access controls, audit trails, and data governance standards across all platform components.
• Train end-users on AI tools and natural language interfaces

Primary Duties:

(The below list is not comprehensive, and priorities will change on a day to day basis)

Data Infrastructure & Integration (35–45%)

• Architect and implement Microsoft Fabric/One Lake as our centralized, cloud-native data platform including Lake houses, Delta tables, Dataflow Gen2 pipelines, and Direct Lake semantic model configuration.
• Build governed ingestion pipelines from Yardi ERP (via Data Freedom SQL abstraction layer and/or MCP), SharePoint (Excel templates, PDFs), Argus, CoStar, Green Street, Measurabl, and other CRE data sources.
• Implement schema validation, business rule enforcement, and data quality monitoring at the ingestion layer, ensuring that only validated, approved data is promoted to the authoritative One Lake layer.
• Design data models that normalize and contextualize information across disparate systems, aligned with the Data Freedom property master as the referential integrity anchor.
• Establish and maintain data governance standards, documentation, data lineage tracking, and change management processes.
• Migrate Power BI semantic model from on-premises gateway (WDSSRV) to cloud-native Fabric refresh eliminating single point of failure.

AI Agent Development & Automation (35–45%)

• Design and deploy AI agents and natural language interfaces using the Microsoft Copilot and Azure OpenAI stack (Azure AI Search, Azure OpenAI Service, Copilot Studio, Power Automate), governed within our Microsoft 365 tenant with no external data transmission.
• Build and maintain the Ask AI agents (Teams + Outlook integration), connecting it to the Azure AI Search vector index over One Lake Delta tables and SharePoint metadata-tagged documents.
• Build API integrations and custom connectors enabling AI systems to query structured (One Lake) and unstructured (SharePoint metadata-tagged documents) data sources.
• Develop automated workflows for recurring report distribution, data collection, validation…