Analytics Data Engineer

MedSrvChattanooga, TN
$65,000 - $75,000Onsite

About The Position

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

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

Benefits

  • Competitive benefits
  • paid time off
  • 401k with match
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