Role Description The Trauma Data I Specialist completes a variety of Trauma, Burn, and Acute Care Surgery Registry tasks, including: - Data collection - Data entry and retrieval - Data quality and integrity - Data analysis - Display and statistical conversion for the purpose of research, performance improvement, injury prevention, education, billing, and outcome measurements Obtains, abstracts, and reports appropriate concurrent and retrospective patient data on Trauma, Burn, and Acute Care Surgery patients. Uses Abbreviated Injury Scaling (AIS) coding of injuries and ICD10 coding of diagnoses and procedures to maintain the Trauma, Burn, and Acute Care Surgery patient databases. Upholds the mission, vision, values, and customer service standards of the UC Health System. Qualifications - Experience in data collection and analysis - Knowledge of AIS and ICD10 coding - Strong attention to detail Requirements - Ability to work independently and as part of a team - Excellent communication skills - Proficiency in data management software Benefits - Competitive salary - Health and wellness programs - Retirement plans Company Description As the pioneering hospital of UC Health, Greater Cincinnati’s academic health system, UC Medical Center has served greater Cincinnati and Northern Kentucky for nearly 200 years. Each year, hundreds of thousands of patients receive care from our world-renowned clinicians and care teams utilizing the most advanced medical knowledge and technology available. UC Medical Center has 724 licensed beds and more than 5,800 employees.

• Own data architecture end-to-end. Define how we capture, model, and serve critical business data then implement it in production. You’ll make architectural decisions around storage formats, compute patterns, and SLAs that balance cost, scalability, and consistency. • Build mission-critical pipelines. Develop and operate streaming and batch data workflows that process high-volume events across multiple domains user activity, transactions, experimentation, marketing performance, and operational telemetry with tight guarantees for latency, completeness, and accuracy. • Design and implement canonical models. Create domain-oriented data models that serve as the source of truth for analytics, ML, and real-time applications. Establish and enforce modeling standards, ownership boundaries, and data contracts across teams. • Enforce data quality at scale. Build tests, lineage, monitoring, and reconciliation systems that make every dataset observable and every anomaly actionable. • Automate operational workflows. Partner with business systems and platform teams to eliminate manual data handoffs and reconcile data across services, warehouses, and external systems. • Enable insights and experimentation. Support analytics, ML, and product engineering teams by exposing high-quality, low-latency data through semantic layers, APIs, and real-time query systems.

• Collaborate and partner with stakeholders, and decision-makers to prioritize, design, and fulfill their data analytics needs. • Identify KPIs driving business growth and performance and work closely with the business teams to define them. • Work across the data stack to design and build various data products (pipelines, data models, analysis) that enable the business to make data backed decisions. • Help garner trust in data from the business by documenting analytics best practices, assisting with data discoverability, governance, and accuracy. • Promote and drive a self-service data culture by building easy to use analytics products and teaching the technical and non-technical users how to use them. • Communicate progress, risks, and completion of projects to stakeholders.

• Design, build and evolve the company’s data platform architecture. • Develop and maintain ETL/ELT pipelines, including API extraction, transformations, incremental loads and CDC (Change Data Capture). • Model data for Data Warehouse, Data Lake and Data Marts, applying dimensional modeling (Star Schema, Snowflake Schema). • Implement batch and streaming processing with event-driven architecture and asynchronous processing. • Ensure database performance and reliability: relational modeling, indexes, partitioning, query optimization and replication. • Establish Data Quality, Data Lineage, Data Catalog and Data Governance practices. • Orchestrate pipelines and handle historical data with versioning and traceability. • Implement pipeline observability: logs, metrics, monitoring and alerts. • Provide reliable and near real-time data for dashboards, operational analytics and business intelligence. • Prepare the data foundation for future Machine Learning initiatives. • Automate processes and deployments using GitHub Actions and Docker. • Collaborate with product, engineering and business teams to define requirements and KPIs.