Senior Financial Data & Analytics Engineer

Required Qualifications (as evidenced by an attached resume):
Bachelor's Degree (foreign equivalent or higher) in Data Science, Computer Science, Information Systems, Finance, Economics, or related field. Five (5) or more years of full-time experience in data engineering, data analytics, or financial analytics in a healthcare or enterprise environment. Experience working with SQL and relational database systems, data warehouse design (e.g. dimensional modeling, star schema) ETL/ELT pipeline development, API integration and data ingestion, and Tableau or equivalent BI platforms. Experience integrating data across ERP systems (e.g. PeopleSoft), HR systems, and operational/clinical datasets. Knowledge of physician productivity metrics (e.g. wRVUs) and healthcare financial data structures. 

Preferred Qualifications: 

Master's degree (foreign equivalent or higher) in Data Science, Analytics, Health Informatics, Finance, or related field. Experience with modern data stack technologies (e.g. Snowflake, Databricks, Azure/AWS data services). Experience working with Python, R, or similar programming languages for analytics or automation. Familiarity with benchmarking datasets (e.g. Vizient, MGMA, AAMC). Experience working in academic medical centers of healthcare system environments. Knowledge of data governance, HIPPA, and secure data architecture practices. Experience working with financial statements, budgeting, forecasting, and accounting principles. Experience using AI assisted analytics platforms or features within tools such as Tableau Power BI or Excel. 
 

Brief Description of Duties: 

The Senior Financial Data & Analytics Engineer designs, develops, and maintains enterprise financial data infrastructure and analytics solutions for the School of Medicine. This includes building data pipelines, integrating enterprise systems, developing predicative and descriptive analytics, and delivering executive-level dashboards. The incumbent operates under general direction and exercises significant independent judgement in architecting scalable, secure, and high-performance analytics solutions that support institutional financial strategy. The ideal applicant will have strong analytical, programming, and problem-solving skills. In the performance of all job duties, at all times be responsible for delivering optimal customer service, protecting institutional data and privacy, and ensuring the excellent delivery of solutions and services by following divisional and University policies, procedures, and processes. 

Duties:

Data Engineering & Integration Architecture:

• Design and implement a centralized financial data warehouse integrating PeopleSoft, HR, CPMP, Report Center, SOMDASH, and external data sources.
• Develop and maintain ETL/ELT pipelines for structured and semi-structured data.
• Build and manage API-based data ingestion pipelines.
• Ensure scalability, performance, and reliability of data architecture.
• Collaborate with IT and enterprise data teams on infrastructure and platforms design. 

Advanced Financial & Clinical Analytics:

• Develop analytics models for physican productivity (wRVUs), reimbursement, and compensation.
• Apply benchmarking data (e.g. Vizient) to comparative performance analysis.
• Build predictive and trend models to support financial planning.
• Analyze payer mix, revenue cycles, and cost structures across departments.

Data Modeling & Financial Intelligence:

• Design data models supporting funds flow, ROI analysis, and All-Funds financial reporting.
• Develop multi-dimensional datasets integrating clinical, academic, and financial data.
• Create standardized metrics and definitions for institional reporting.
• Support scenario modeling and financial forecasting.

Business Intelligence & Data Visual