Platform, AI & Data Solutions Architect

Data Engineer And Solutions Architect

Westcore is a fully-integrated commercial real estate investment company with institutional scale and capabilities that operates with the speed and adaptability of opportunistic entrepreneurs. Westcore has a dynamic track record of real estate investing going back to its founding in 2000. We focus on well-located industrial properties in the United States. We are a vertically integrated company with expertise in all facets of real estate investment management: acquisitions, finance, asset management, leasing, construction, and building operations.

To better support our IT Department with the anticipated doubling of our portfolio over the next few years, Westcore is 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 Westcore's "talk to your data" initiative.

Westcore is developing modern data infrastructure to run our business more efficiently. We are seeking a hands-on Platform & Data Solutions Architect to design and build the shared data model, platform backbone, and integrations that power a growing suite of applications, and to lead the transformation of our information into a structured, well-governed, AI-enabled foundation.

You will be the technical backbone of the platform and the company's one-stop shop for data: architecting the data structures, building shared services and integrations, enabling safe and data-governance compliant data architecture for applications, and collaborating on delivering or thoughtfully sourcing AI capabilities. This is a rare opportunity to own a modern platform end-to-end in a fast-moving, entrepreneurial environment.

• Data architecture. Design and own a shared, canonical data model and a well-partitioned relational database where multiple applications read and write safely. You build real applications and the data foundation beneath them, not read-only dashboards.
• Data and document strategy. Lead the transformation of the company's information, both structured system data and unstructured documents, into structured, governed, AI-ready data. This includes a strategy for content that today lives across systems such as SharePoint and Office
  365, working with multiple stakeholders on the business and compliance decisions about what to bring in, when, and how.
• Platform backbone and security. Build and own shared platform services (authentication and SSO, administration and role management, document processing, and an AI and natural-language gateway). Implement access controls such as row-level security in the database as the platform's security backbone.
• Enablement and cross-department support. Support application development across every department, not only those already building. Some teams have a hands-on builder who can drive their own app and needs backend support to get it over the line, other teams know what they want but need someone more hands-on to build it with them. Provide both modes of support, reading what each department needs and flexing your involvement to match.
  
  Set the guardrails (source control, environments, secrets, and a lightweight review process) that let capable builders ship autonomously without compromising data integrity.
• Integration and source-of-truth discipline. Own integrations to our systems of record, including Yardi and its data layer, which remains our accounting system of record, and to market-data and vendor sources via APIs and connectors. Handle any write-back through approval-gated workflows, enable integrations of sources of truth.
• Support for accounting and finance. Enable AI and automation for finance and accounting processes with deterministic standard of validation.
• Model-agnostic AI delivery and build-vs-buy. Deliver custom AI and agentic solutions, including natural-language "talk to your data" and retrieval (RAG) capabilities, where in-house is the right call. Evaluate build-vs-buy per use case, including overseeing technical due diligence for any AI solutions.

• Bachelor's degree in Computer Science, Information Systems, Software or Data Engineering, or equivalent practical experience.
• Demonstrated experience designing and building production data platforms on a modern relational database, including schema design, access controls such as row-level security, and databases that serve multiple applications.
• Hands-on full-stack development and modern web application delivery, including server-side and API work.
• Practical experience building AI and LLM features (agentic workflows, retrieval/RAG, natural-language-to-data, and document processing) beyond basic prompting.
• Experience integrating enterprise systems via REST APIs and building or consuming ETL pipelines, with strong source-of-truth and data-governance discipline.
• Working familiarity with the…