Research Data Engineer
About The Position
The Center for Health Disparities Research (CHDR), led by Director Dr. Amy Kind and Deputy Directors Drs. Nicole Rogus-Pulia and Andrea Gilmore-Bykovskyi, invites applications from highly motivated individuals to join our dynamic, growing, and multidisciplinary team. As a Research Data Engineer with CHDR, you will have the opportunity to contribute to research projects with far-reaching influence, including the largest study of its kind on social determinants of health in the context of Alzheimer's disease. Broadly, our work is focused on mechanisms of health disparities - the ways fundamental factors such as race, ethnicity, and identity interact with a complex array of geopolitical, socioeconomic, health care, cultural, social, psychological, physiological, genetic, and cellular factors to produce different population health outcomes. We are seeking a highly motivated individual to join our team and provide expertise on data pipeline management and database engineering within a research environment. This position will work with CHDR scientists and Data Operations peers to optimize current processes related to a relational database used for scholarly research. The Research Data Engineer will join a dynamic team that works on multi-unit projects, both internal and external to UW-Madison. These projects range from the management of a robust relational database in an Azure cloud platform to produce datasets for distribution among a 24-site consortium, the creation of data visualizations for publications, and working with data analysts and IT professionals in moving forward research in health disparities. Under the guidance of one of CHDR’s Research Scientists, the successful Research Data Engineer will assist the CHDR scientific team to optimize processes within a large-scale relational database to prepare and analyze data using reproducible pipelines for research purposes. Processing would include data from multiple sources, including geographically-linked metrics of socioeconomic factors and measures of health outcomes, furthering research on and interventions in the social determinants of health.
Requirements
- Intermediate or advanced experience using R or Python to perform data processing/ETL tasks.
- Comfortable working in a command line environment.
- Demonstrated experience with large scale relational database management systems.
- Demonstrated experience in a research setting.
Nice To Haves
- Intermediate or advanced experience using R or Python and/or SQL to perform data processing/ETL tasks.
- Demonstrated experience with data pipelining, task automation, data structures and algorithms.
- Experience developing, debugging, and testing data processing pipelines.
- Familiarity with Git or other version control systems.
- Familiarity with containerization and/or virtual environment management using Docker, conda, venv, etc.
- Familiarity with REDCap.
- Experience working with PHI and other sensitive data.
Responsibilities
- Develops, constructs, tests, and maintains architectures for large-scale data management and analysis
- Implements data analysis steps in collaboration with data scientists, statisticians, and/or other researchers and may use technologies that support data at scale
- Organizes both data preparation and analysis steps into reproducible pipelines that can process similar data sets automatically
- Selects appropriate technologies and optimizes pipelines for performance
- Prepares data sets for current and future analysis including cleaning/quality assurance, transformations, restructuring, and integration of multiple data sources and may use technologies that support data at scale
Benefits
- generous vacation, holidays, and sick leave
- competitive insurances and savings accounts
- retirement benefits
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What This Job Offers
Job Type
Full-time
Career Level
Entry Level
