AI Data Architect

Essential Functions: 

• Data Pipelining & Infrastructure 

• Build, optimize, and maintain the data pipelines that feed BAA's AI solutions, connecting both structured and unstructured data from enterprise data lakes (Snowflake), ERP systems (Epicor), CRM (Salesforce), and document repositories (SharePoint). 

• Automate the ingestion and structuring of unstructured data (PDFs, manuals, contracts, emails) to enable highly accurate Retrieval-Augmented Generation (RAG) architectures. 

• Map complex enterprise data schemas to identify the exact tables, fields, and payloads required to support the cognitive architecture designed by the AI Studio Lead. 

• Build and maintain development and testing environments that allow the Studio to experiment rapidly without risking production systems. 

• Security, Governance & Compliance 

• Design and implement strict Role-Based Access Control (RBAC) and enterprise security protocols within the AI environment — ensuring AI agents only query data explicitly authorized for the specific end-user. 

• Prevent data leakage across departments by structurally constraining what each AI model can access at the pipeline level, not just the application level. 

• Serve as the formal infrastructure checkpoint before cognitive architecture development begins — confirming pipeline stability, RBAC compliance, and data integrity prior to any build phase commencing. 

• Monitor data flows for system drift, broken APIs, or context collapse, ensuring AI outputs remain accurate and safe for both office and factory floor operations. 

• Establish and maintain Service Level Agreements (SLAs) for pipeline uptime — detecting and communicating failures before end-users are impacted. 

• Platform Integration & IT Liaison 

• Develop secure API integrations between enterprise AI platforms and BAA's existing technology stack. 

• Serve as the primary technical liaison to BAA IT and Global Data Architecture, ensuring infrastructure provisioning, database access, and cloud networking meet enterprise standards. 

• Advocate for the AI Studio's technical needs within the broader IT governance ecosystem. 

• Implement automated alerting systems for data pipeline failures. 

Knowledge, Skills, Abilities: 

• Deep technical expertise working with SQL, enterprise data lakes, and ETL/ELT pipeline construction. 

• Strong proficiency in building and securing API integrations between disparate cloud and on-premises systems. 

• Experience implementing strict security frameworks and Role-Based Access Controls (RBAC) at the database and application layer. 

• Exceptional troubleshooting and debugging skills — able to independently diagnose integration issues across legacy platforms without waiting for vendor support. 

• Comfort operating in a small, fast-moving team where you own entire workstreams end-to-end without layers of specialization or handoffs. 

Minimum Requirements: 

• Bachelor’s degree and minimum of 6 years of related work experience required. 

• An equivalent combination of education and/or related work experience equal to 10 years will also be considered. 

• 3+ years of experience in data engineering, data architecture, or database administration within an enterprise environment.