Data Engineer

Job Summary

About Us: The Institute on Aging is a research unit whose mission is to promote the health and well-being of the adult and aging populations through excellence in multidisciplinary research, education, and outreach. For the past 2.5 decades, the IOA has led a large national longitudinal study of health and well-being in 11,000+ American adults. Known as MIDUS (Midlife in the US), the study examines influences of emotion, personality, biology, genetics, and neuroscience as well as the influences of work and family life, class, culture, race/ethnicity, and historic events to understand trajectories of health across the decades of adult life. Data from MIDUS are publicly available and are used by thousands of researchers all over the world. To date, 2,000+ publications have been generated across diverse fields. The study is also used by graduate students around the U.S. to complete masters and doctoral degrees.

The Data Engineer will process and clean MIDUS data delivered to the Administrative Core by the data collection sites (Pennsylvania State University, Brandeis University, UW-Madison and Georgetown) ; Prepare data, metadata, and documentations for new or updated MIDUS data releases; Maintain and update the MIDUS Colectica Portal; Respond to data or documentation requests for non-public released MIDUS data.

Key Job Responsibilities:

• Organizes both data preparation and analysis steps into reproducible pipelines that can process similar data sets automatically
• Implements data analysis steps in collaboration with data scientists, statisticians, and/or other researchers and may use technologies that support data at scale
• May supervise the data-to-day activities of staff and resolves routine personnel issues
• 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
• Serves as an institutional subject matter expert and liaison to key internal and external stakeholders regarding automated data management and analysis at scale for research and represents the interests of large-scale data management and analysis for research
• Develops, constructs, tests, and maintains architectures for large-scale data management and analysis
• Selects appropriate technologies and optimizes pipelines for performance