Analytics Data Engineer

MedSrv•Chattanooga, TN

10d•$65,000 - $75,000•Onsite

About The Position

As the healthcare industry continues to grow, so does the need for consistent, effective Revenue Cycle Management. At MedSrv, we believe it takes more than just technology and technical expertise to make a difference. It takes innovation, a willingness to adapt, and a passion to be the best. We are the difference in Revenue Cycle Management – guided by faith and committed to serving with integrity and compassion. We are seeking an Analytics Data Engineer to design, build, and maintain scalable data models and pipelines that support advanced analytics and AI-driven solutions in the healthcare space. In this role, you will play a key part in enabling AI agents to interact with complex healthcare datasets, including Revenue Cycle Management (RCM) workflows to deliver meaningful insights to end users. You will collaborate across data engineering, analytics, and business intelligence teams, using modern tools and frameworks to ensure data accuracy, scalability, and accessibility. By preparing and modeling healthcare data for AI, you will help develop intelligent solutions that enhance operational efficiency, streamline billing processes, and improve decision-making across the revenue cycle.

Requirements

Bachelor’s or Master’s in Computer Science, Data Engineering, Analytics, or related field
3+ years of experience in data engineering, data modeling, analytics, or BI
Strong Python skills (ETL, analysis, visualization)
Advanced SQL proficiency
Experience with Microsoft Fabric and Power BI
Advanced Excel skills
Familiarity with GitHub, CI/CD, and version control
Experience building scalable data pipelines and working with diverse data sources
Ability to perform complex data analysis and create dashboards

Responsibilities

Data Modeling & Architecture Design and implement semantic data models in Microsoft Fabric. Optimize data structures for scalability, performance, and usability.
ETL & Data Integration Build and maintain ETL workflows using SSIS, Python, and SQL. Integrate data from diverse sources into centralized analytics repositories.
Analytics & Reporting Conduct advanced analysis using Python and SQL. Build dashboards and reports in Power BI, Excel, and Fabric. Provide ad hoc insights to stakeholders.
Data Validation & Quality Assurance Create validation rules ensuring data accuracy and consistency. Develop automated testing frameworks for ETL processes and data models.
AI Enablement Prepare and structure data for AI agent training and interaction. Collaborate with AI and data science teams to ensure readiness for model development.
Automation & CI/CD Use GitHub and CI/CD pipelines for version control and deployment. Automate workflows to reduce manual effort and enhance reliability.
Collaboration & Governance Partner with analysts, engineers, and operations teams to meet business needs. Ensure adherence to data governance, security, and compliance standards.