What are the responsibilities and job description for the Data Architect & AI Solutions Engineer position at 7033 Spiras Health?
JOB DESCRIPTION
Job Title: Data Architect & AI Solutions Engineer
Work Location: Brentwood, TN
Department: Operations
Reports To: VP, Data and Information Technology
FLSA: Non-Exempt
Who We Are: Purposeful, Progressive, Dynamic
Spiras Health is a value-based, nurse practitioner led clinical provider of care-at-home and other health-related services to individuals with complex and polychronic needs. Spiras’ comprehensive approach to care delivery includes a combination of home-based services, telehealth, and two-way digital communications. Proprietary predictive modeling identifies and assesses individuals with an elevated probability of avoidable costs. Spiras Health then develops actionable plans of care, addresses barriers including social determinants of health, and delivers high quality patient care in collaboration with the patient’s treating physicians. Spiras’ innovative multi-modal care approach delivers improved satisfaction and clinical metrics as well as financial savings to our partners, through a geographically and economically scalable delivery model. Our culture is anchored on a promise of full accountability and integrity in everything we do.
How We Serve
We are seeking an experienced and highly skilled Data Architect & AI Solutions Engineer to join our team and lead the design, development, and deployment of a modern data warehouse solution. The ideal candidate will have extensive experience with data architecture, data warehousing, and advanced analytics, including the implementation of AI models for predictive analysis. As a Microsoft-centric shop, the role requires deep expertise in Microsoft technologies such as Azure Data Services, Power BI, and SQL Server.
This individual will play a critical role in transforming raw data into actionable insights and predictive models that drive business decisions. The ideal candidate should also have experience in guiding cross-functional teams through the complexities of large-scale data systems, AI model deployment, and data-driven decision-making processes.
