Senior Data Platform & Healthcare AI Analytics Engineer

About MindCare Solutions
MindCare Solutions is a premier provider of behavioral health services, supporting hospitals, emergency departments, inpatient behavioral health units, long-term care centers, correctional settings and various other clinical settings.

We partner with healthcare organizations to deliver top-tier, end-to-end behavioral health solutions, integrating evidence-based care pathways, advanced technology, and exceptional providers. Our organization is committed to enhancing mental health care accessibility and efficacy through innovative solutions and strong partnerships, ensuring that high-quality, patient-centered care is delivered to improve the health and well-being of the communities we serve. 

Senior Data Platform & Healthcare AI Analytics Engineer

Location: Remote

Compensation: negotiable

Position Overview

We are seeking a Senior Data Platform & Healthcare AI Analytics Engineer to architect, build, and evolve an AI-augmented healthcare data and analytics ecosystem.

This is a high-impact full-stack data platform role for a technical builder who understands that behind every data point is a patient. Responsible for designing the pipelines, platforms, and analytics models that transform fragmented healthcare data into a scalable intelligence layer supporting analytics, operational decision-making, and AI-driven insights.

Creating an AI-first architecture leveraging Microsoft Fabric, Azure AI Services, third-party AI tools, (ChatGPT, Claude), Azure Synapse, Azure Data Factory, Azure SQL, and Power BI, this role will integrate clinical, financial, and operational data sources to create a unified Healthcare Intelligence platform. The platform will support improved patient outcomes, operational efficiency, revenue cycle performance, and compliance with HIPAA and healthcare regulatory standards.

Beyond building infrastructure, this role is expected to enable business insights for the performance deep analytical work to identify trends, inefficiencies, and revenue opportunities, translating complex healthcare data into actionable insights that improve how the organization delivers care.

Core Responsibilities

Data Platform Architecture & Engineering

• Architect and operate the organization’s Azure / Microsoft Fabric healthcare data platform
• Design scalable ETL/ELT pipelines integrating EMR/EHR systems via HL7, FHIR APIs, and direct integrations
• Design and develop secure, scalable EMR interface APIs enabling bi-directional interoperability between Microsoft Dynamics 365 Health Cloud and partner EMRs, supporting clinical workflows (orders, documentation, patient context, care coordination) using HL7, FHIR, and modern API-based integration patterns.
• Build, extend and maintain the lakehouse and warehouse environments using Microsoft Fabric, Azure Synapse, and Azure Data Lake
• Integrate healthcare data from EMRs, revenue cycle systems, telehealth platforms, scheduling systems, and external healthcare datasets
• Ensure reliability, scalability, and performance of the enterprise data platform

Analytics Platform & Power BI Architecture

• Design and manage the Power BI analytics platform, including governance, workspaces, and enterprise datasets
• Build Power BI semantic models, reusable datasets, and reporting frameworks
• Deliver executive and operational dashboards supporting clinical, operational, and financial leaders
• Enable self-service analytics across the organization

Healthcare Analytics & Business Intelligence

Analyze healthcare data to generate insights across core operational domains, including:

• provider productivity and utilization
• patient access and scheduling performance
• encounter lifecycle and clinical workflow efficiency
• revenue cycle and claims performance
• payer reimbursement and financial trends

Translate complex healthcare datasets into clear insights that improve patient outcomes, operational performance, and financial sustainability.

AI-Enabled Healthcare Analytics

Design and implement the data foundation for AI-driven healthcare analytics, including:

• predictive models for patient no-shows, provider capacity