Research Data Engineer

University of Wisconsin Madison•Madison, WI

2d•Hybrid

About The Position

The Center for Health Disparities Research (CHDR) is seeking a highly motivated Research Data Engineer to join their dynamic, growing, and multidisciplinary team. This role will contribute to research projects focused on the mechanisms of health disparities, including a large study on social determinants of health in the context of Alzheimer's disease. The Research Data Engineer will provide expertise in data pipeline management and database engineering within a research environment, optimizing processes for a relational database used for scholarly research. The position involves working with CHDR scientists and Data Operations peers on multi-unit projects, managing a relational database on an Azure cloud platform, producing datasets for a 24-site consortium, creating data visualizations, and collaborating with data analysts and IT professionals. Under the guidance of a Research Scientist, the successful candidate will assist the CHDR scientific team in optimizing processes within a large-scale relational database to prepare and analyze data using reproducible pipelines. This includes processing data from multiple sources, such as geographically-linked metrics of socioeconomic factors and health outcomes, to further research on and interventions in the social determinants of health. This position is full or part-time (80%-100%) and may require some in-person work at a designated campus location, with the possibility of some remote work.

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.
Bachelor's Degree Preferred
Excellent organizational skills, strong attention to detail, and the flexibility to work both independently and collaboratively.
Excellent interpersonal skills related to communicating and documenting steps in the data processing pipeline.
Must provide proof of work authorization and eligibility to work in the United States.

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, potentially using 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, potentially using technologies that support data at scale.