COMPANY OVERVIEW

Voted #1 EHR by PC Mag, WRS Health delivers a fully integrated cloud based EMR and practice management solutions to its clients. We bring solutions to physicians by providing constant enhancement of our products and services including EHR, practice management, marketing, patient coordination and billing.

QUALIFICATIONS:

• Bachelor’s or Master’s in Computer Science, Data Engineering, or related field.
• 8–12+ years of experience in data architecture with 3–5 years in a technical leadership role.
• Proven experience architecting AWS-based data lakes and analytics pipelines.
• Deep understanding of healthcare data standards (FHIR, HL7) and working with FHIR JSON objects in large-scale systems.
• Expertise with embedding and vectorization models, semantic search, and managing vector storage solutions.
• Hands-on experience with Amazon S3, Mountpoint for S3, and optimizing S3-based workloads for performance and cost.
• Strong background in data security, encryption, access control, and compliance frameworks (HIPAA, HITRUST).

Preferred Qualifications

• AWS certifications (e.g., AWS Certified Big Data or Data Analytics – Specialty).
• Familiarity with open-source vector databases (e.g., FAISS, Weaviate) and MLOps pipelines.
• Experience in clinical systems integration, claims processing, or population health analytics.

This is an independent contractor position.
Job Type: Full-time
Location: Remote
Hours: Available during standard US business hours (9am-5pm EST or 8:30am-4:30pm EST)
This job description is intended to describe the general requirements for the position. It is not a complete statement of duties, responsibilities or requirements. Other duties not listed here may be assigned as necessary to ensure proper operations of the department.
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JOB PURPOSE AND ROLE

WRS Health is seeking a hands-on and strategic Director of Data Architecture to lead our data platform initiatives on Amazon Web Services (AWS) with a strong emphasis on healthcare data, data lake strategy, and AI enablement. You will architect and manage scalable, secure, and high-performance data systems that support both traditional analytics and modern ML workloads, including embedding models and vectorized data retrieval.