The Michigan Trauma Quality Improvement Program, or MTQIP, is a statewide quality improvement collaborative focused on improving trauma care, outcomes, and data-driven performance across participating Michigan trauma centers.
MTQIP is seeking a Clinical Registry Data Architect to support clinical registry data infrastructure, report exports, data pipelines, automation, and program-wide technical data needs. This position will play a key role in maintaining and improving SQL-based report exports from registry platforms, including Snowflake Reader, and supporting the development of streamlined workflows for data submission, validation, reporting, and analysis. In addition, MTQIP is looking to develop extracts from electronic medical record such as EPIC Clarity, and proficiency in data science concepts such as Large Language Models (LLM).
This role is well suited for a data professional who enjoys working at the intersection of healthcare, technology, quality improvement, and applied data engineering. The ideal candidate will be comfortable working with complex clinical data, translating registry and measure specifications into technical logic, writing and maintaining SQL, supporting data quality checks, and collaborating with clinical, operational, and technical stakeholders.
This position will support MTQIP's clinical registry reporting needs, including report exports from vendor systems, data transformation processes, automation, data validation, ad hoc data and technical projects, and potential future data extraction from electronic health record systems such as Epic. The person in this role will work closely with MTQIP leadership, program managers, analysts, clinicians, participating hospitals, registry vendors, and institutional technical teams.
A typical week may include SQL development, Snowflake query development, data pipeline maintenance, troubleshooting registry export logic, documenting technical specifications, validating data quality, supporting automation, responding to ad hoc technical data requests, participating in stakeholder meetings, and helping translate clinical measure logic into reliable, reproducible data processes.