Build and maintain scalable ETL pipelines for experimental, biological, and pharmacological data
Integrate data from multiple sources such as GeneData, LabVantage, CROs, and cloud platforms into unified analytical environments
Automate recurring workflows to ensure data quality, accessibility, and reproducibility
Collaborate with data scientists to implement and operationalize AI and machine learning models into production-ready pipelines on cloud and HPC environments
Monitor and maintain deployed models to ensure performance, scalability, and traceability
Develop scripts, APIs, and workflows to connect laboratory systems such as GeneData Biologics and LabVantage with central analytics environments
Act as the technical link between Discovery, IT, and Data Science teams
Translate scientific and technical needs into robust, scalable solutions
Provide responsive technical support to scientists to ensure data accessibility and workflow reliability.
Requirements
MSc in Computer Science, Data Science, Computational Biology, or a related field
Three to five years of relevant experience in data engineering or scientific data environments
Proficiency in Python and version control systems such as Git
Experience with AWS services including S3, Lambda, ECS, Glue, and Step Functions
Strong understanding of data integration, workflow automation, and pipeline design
Excellent communication skills with the ability to explain technical solutions clearly to scientific and technical colleagues
Proactive problem solver who can adapt quickly to changing priorities.
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