Data Engineer

Healthfuse is a healthcare technology company that uses technology, analytics, and services to help hospitals manage vendors and revenue cycles. The Data Engineer will design and maintain a Microsoft Fabric-based data platform, develop data pipelines and Azure Service Bus integrations, support ETL and SQL workflows, and ensure data quality for internal and client-facing reporting.

Responsibilities:

• Design, build, and maintain data pipelines and lakehouses within Microsoft Fabric
• Architect and implement Azure Service Bus integrations to enable bidirectional communication between the web platform and Fabric — including event triggers for spec table changes, file error/warning notifications, report refresh completions, and portal-facing report data relay
• Develop and maintain event-driven processing workflows to replace manual, BIA-driven file handling
• Collaborate with platform and product stakeholders to ensure Fabric outputs are accurately surfaced in the web portal
• Design and develop ETL workflows and datasets using Alteryx or an equivalent data transformation tool
• Write complex SQL queries across multiple tables using advanced joins, CTEs, and window functions (MSSQL)
• Prepare technical specifications and documentation for ETL workflows supporting data and reporting pipelines
• Perform end-to-end data validation to ensure integrity across all pipeline stages
• Support identification and development of hospital clients' baseline revenue cycle vendor performance datasets
• Contribute to the customization and development of Healthfuse's client reporting packages
• Assist in the research and analysis of quantitative and qualitative performance data for healthcare revenue cycle vendors

Requirements:

• 3–5 years of professional Data Engineering experience
• Hands-on experience with Microsoft Fabric (lakehouses, pipelines, notebooks)
• Experience with Azure Service Bus or equivalent event-driven messaging services
• Strong proficiency in MSSQL and complex SQL development
• Experience with Python for data engineering and pipeline automation
• Experience with Alteryx or an alternative ETL/data transformation tool (e.g., dbt, SSIS, Azure Data Factory)
• Strong analytical, problem-solving, and troubleshooting abilities
• Healthfuse is not able to sponsor employment visas — including new sponsorship, transfers, or extensions — at this time or in the future. This applies to all visa categories, including time-limited statuses such as OPT or STEM OPT extensions that would require future employer sponsorship
• Bachelor's degree in Computer Science, Data Engineering, Information Systems, or a related field, or equivalent practical experience
• Proficiency with Microsoft Fabric, Azure services, SQL, Python, and ETL/ELT technologies
• Excellent verbal and written communication skills
• Ability to work independently and effectively in a fast-paced, high-growth environment
• Strong analytical and problem-solving skills with a focus on data quality, accuracy, scalability, and platform reliability
• Experience designing event-driven and microservices-based data architectures
• Experience integrating AI and machine learning capabilities into data platforms and pipelines, including Azure AI Services, OpenAI APIs, and embedded ML solutions within Microsoft Fabric
• Familiarity with natural language querying and AI-assisted analytics tools that enable users to access insights without relying on technical resources
• Experience building or supporting LLM-powered workflows, AI-enhanced data products, or intelligent automation solutions within a data engineering environment
• Experience with GitHub and/or Azure DevOps (ADO) for version control and collaborative development
• Healthcare and Revenue Cycle Management industry experience preferred
• Strong project management skills with the ability to define, manage, and continuously improve processes independently
• Strong written communication skills, including the ability to clearly document technical specifications, architecture decisions, and development standards
• Self-motivated, highly organized, detail-oriented, and collaborative