Senior Specialist: Data Engineer Upstream Biologics

Senior Specialist, Data Engineer (Digital Insights, DSCS Digital Technologies)
Responsibilities

• Build and maintain robust, scalable data pipelines ingesting experimental and process data from upstream biologics source systems.
• Deliver analysis-ready datasets supporting process characterization models, scale-up predictions, multivariate analytics, and high-throughput process development workflows.
• Map instrument outputs and experimental results to ensure ontology alignment and interoperability across upstream data sources.
• Develop and maintain data visualizations, dashboards, and reports for upstream scientists.
• Support system-of-record standards and consistent data entry practices.
• Identify/flag data quality issues, metadata gaps, and inconsistencies to improve upstream data capture.
• Collaborate with scientists and engineers to translate evolving data needs into pipeline requirements; coordinate seamless data handoffs.
• Maintain and version pipeline code in Git Hub with standards for code review, documentation, and deployment.
• Demonstrate strong interpersonal, communication, and collaboration skills; thrive in a multidisciplinary team environment.

• Ph.D. or M.S./B.S. in Computer Science, Data Science, Engineering, Chemistry, Physics, Biology, Pharmaceutical Sciences, Molecular Modeling, or closely related field (with relevant years of industrial/pharmaceutical experience for M.S./B.S.).

• Proficient in Python and/or R (Jupyter, Posit/RStudio, or VS Code).
• Solid SQL for relational DBs and data warehouses.
• ETL/ELT and pipeline building in scientific/pharmaceutical contexts.
• Git/Git Hub version control and collaborative development.
• Cross-functional teamwork; motivated to learn and apply scientific curiosity.

• Upstream biologics unit operations experience (cell culture, bioreactors/scale-up, media/feed optimization, harvest/clarification).
• Databricks/Delta Lake; workflow orchestration.
• Visualization tools (Streamlit, Shiny, PowerBI, Spotfire, Tableau).
• Ontology frameworks/standard data models (e.g., Allotrope, ISA-88, OPC-UA) and structured schema mapping.
• DoE and process characterization for CPP/CQA statistical analysis.

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