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AI & Data Architect

Job in Asheville, Buncombe County, North Carolina, 28814, USA
Listing for: MAHEC
Full Time position
Listed on 2026-10-15
Job specializations:
  • IT/Tech
    Data Engineering, Information & Knowledge Management
Salary/Wage Range or Industry Benchmark: 80000 - 98000 USD Yearly USD 80000.00 98000.00 YEAR
Job Description & How to Apply Below
JOB SUMMARY:

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.

SPECIFIC RESPONSIBILITIES:
Data Architecture & Integrity
  • 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

AI Strategy & Governance
  • 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

Data Governance
  • 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…

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