AI & Data Architect

Mountain Area Health Education Center - MAHEC•Asheville, NC

5d•Onsite

About The Position

MAHEC's ability to adopt artificial intelligence responsibly and effectively is not just a technology imperative — it is a data imperative, rooted in the people, processes, and systems that produce and govern the data lifecycle. AI tools are only as reliable as the data that underlies them, and in a community health center context, unreliable data carries direct patient safety and privacy implications. The Enterprise Data Architect & AI Readiness Lead owns both sides of that equation. This person is responsible for the architectural integrity of MAHEC's enterprise data systems and for developing the strategic roadmap that guides how the organization evaluates, adopts, and governs artificial intelligence. These two functions are intentionally held in the same role — because decisions about AI adoption and decisions about data architecture are not separable, and treating them as such creates exactly the gaps this role is designed to prevent. This is MAHEC's primary internal authority on enterprise data architecture and AI strategy. The role carries real technical accountability for the quality and trustworthiness of our data, and real strategic accountability for how the organization engages a rapidly evolving AI landscape — with intention, rigor, and fidelity to our mission.

Requirements

5+ years of progressive experience in data architecture, data engineering, or enterprise data management, with demonstrated ownership of a complex, multi-source data environment
Hands-on proficiency with modern cloud data platforms (Microsoft Azure / Synapse Analytics strongly preferred); ability to reason about data models, schemas, and integration patterns at an architectural level
Experience with BI platforms and their underlying data layers; Qlik Sense QVD architecture and data modeling experience strongly preferred
Direct experience developing or implementing AI/ML strategy in an organizational context — meaning you have evaluated vendors, written governance frameworks, or led pilots
Demonstrated understanding of AI limitations, failure modes, and bias risks — particularly in contexts where the underlying data is incomplete or clinically sensitive; the ability to be AI-critical, not just AI-enthusiastic, is non-negotiable given MAHEC's patient safety and privacy commitments
Experience with data governance frameworks: data classification, stewardship models, access controls, and the operational work of turning governance policy into architectural practice
Strong written and verbal communication skills; able to produce a technical architecture document and a leadership-facing strategy memo in the same week

Nice To Haves

Experience in healthcare, public health, FQHC, or social sector data environments; familiarity with HL7/FHIR, EHR data structures, or clinical quality measure data is a meaningful differentiator
Experience managing external data partnerships and integrations (ADT/HIE feeds, state and federal data exchanges, population health data aggregators)
Exposure to federal reporting environments (UDS, eCQMs, HEDIS) where data precision carries regulatory and reputational consequences
Working knowledge of metadata management, data cataloging tools, or master data management practices
Master's degree in computer science, data science, information systems, health informatics, or a closely related field preferred

Responsibilities

Own the design, documentation, and continuous improvement of MAHEC's enterprise data architecture, including the Microsoft Synapse data warehouse, integration patterns, and data lineage across clinical, operational, and financial source systems
Establish and enforce data standards, naming conventions, and schema governance that ensure data is consistent, auditable, and AI-ready across the enterprise
Lead data quality assessment and remediation efforts, developing monitoring systems that surface structural issues before they propagate into analytics outputs or AI model inputs
Manage and govern external data partnerships and integrations, including population health partnerships, ensuring that inbound and outbound data is properly documented, validated, and integrated with appropriate provenance tracking
Serve as the primary technical author of MAHEC's data architecture documentation: entity relationship diagrams, data dictionaries, integration maps, and lineage documentation
Develop and maintain MAHEC's organizational AI strategy, including a phased adoption roadmap calibrated to MAHEC's data maturity, operational context, and mission commitments
Establish an AI governance framework including vendor evaluation criteria, risk assessment protocols, acceptable use policies, and ongoing monitoring standards appropriate to a federally qualified health center environment
Lead evaluation and piloting of AI tools across clinical and administrative contexts, with explicit attention to bias, fidelity, and patient safety; ensure that all new product evaluations include review and negotiation of data use agreements (DUAs), business associate agreements (BAAs), and data residency and retention terms before any organizational commitment is made
Serve as MAHEC's primary liaison to AI vendors and technology partners, translating organizational data architecture into implementation requirements and holding vendors accountable to data standards and governance commitments
Maintain active awareness of the evolving AI regulatory and policy landscape and translate emerging requirements into organizational practice
Serve as a core technical leader within MAHEC's Data & Analytics Governance Committee translating governance policy into architectural and operational practice
Lead the technical dimension of data stewardship: ensuring that governance decisions about data ownership, access classification, and retention are implemented in the architecture — not just documented on paper
Partner with clinical, operational and research leadership to mature the governance framework from its founding phase into a durable institutional capability with clear accountability structures
Develop governance standards specifically applicable to AI: input data documentation, model versioning, output auditing, and decommissioning protocols that ensure AI tools remain accountable throughout their lifecycle