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Life Science Data Engineer
Job in
Madison, Dane County, Wisconsin, 53774, USA
Listed on 2026-07-13
Listing for:
Jobtailor
Full Time
position Listed on 2026-07-13
Job specializations:
-
IT/Tech
Data Engineering, Data Warehousing
Job Description & How to Apply Below
- Collaborate with scientific teams to design scalable data capture systems and user-friendly visualizations of experimental results
- Design and implement database architectures, data models, and automated data pipelines across platforms (e.g., Amazon Web Services, Laboratory Information Management Systems, Benchling, and contract research organization systems)
- Develop structured data models capturing microbial strain metadata, growth conditions, and experimental outputs
- Partner with researchers to standardize experimental metadata, ontologies, and data structures to improve data quality, reproducibility, and traceability
- Build and maintain data warehouses supporting probiotic growth, strain characterization, and clinical datasets
- Develop and implement robust data quality and integrity checks across data ingestion workflows, including integration of externally generated clinical data
- Build pipelines integrating multi-modal datasets (e.g., sequencing, metabolomics, in vitro assays, and clinical endpoints)
- Partner cross-functionally to prototype and deploy digital solutions that enhance research and development efficiency and scalability
- Ph.D. in Data Engineering, Computational Biology, Data Science, or a related field with relevant experience; or M.S. with equivalent professional experience
- Strong Structured Query Language (SQL) skills and experience with database systems such as SQL Server, PostgreSQL, or Oracle
- Proficiency in Python, R, or Spark for data processing and analysis
- Experience working with biological or experimental datasets with complex metadata structures
- Familiarity with statistical or computational analysis of biological systems
- Demonstrated ability to manage multiple projects and work independently in a dynamic environment
- Strong communication and collaboration skills, with experience partnering effectively with scientific teams
- Experience designing scalable data architectures and optimizing data workflows for downstream users
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