Senior Specialist: Data Engineer for Upstream Biologics
Merck
June 26, 2026
Remote friendly (Rahway, NJ)
United States
IT
Responsibilities:
- Build and maintain robust, scalable ETL/ELT data pipelines ingesting upstream biologics experimental/process data.
- Deliver analysis-ready datasets for process characterization models, scale-up predictions, multivariate analytics, and high-throughput workflows.
- Map instrument outputs/results to ensure ontology alignment and interoperability.
- Develop data visualizations, dashboards, and reports for scientists.
- Support system-of-record standards for consistent data entry.
- Identify/flag data quality issues, metadata gaps, and inconsistencies; improve data capture.
- Collaborate with scientists and engineers to translate evolving data needs into pipeline requirements.
- Coordinate with adjacent engineers for seamless data handoffs.
- Maintain/version pipeline code in GitHub (code review, documentation, deployment).
Education (minimum):
- Ph.D. (CS/Data Science/Molecular Modeling/Engineering/Chemistry/Physics/Biology/Pharmaceutical Sciences or related).
- OR M.S. in related field with 2+ years industrial/pharma/relevant experience.
- OR B.S. in related field with 4+ years industrial/pharma/relevant experience.
Required skills/experience:
- Proficient in Python and/or R; comfortable with Jupyter/Posit-RStudio/VS Code.
- Solid SQL for relational databases and data warehouses.
- ETL/ELT pipeline experience in scientific/pharma context.
- Git/GitHub and collaborative development practices.
- Cross-functional teamwork; learning mindset and scientific curiosity.
Preferred skills/experience:
- Upstream biologics unit operations (cell culture, bioreactor, scale-up, media/feed optimization, harvest/clarification).
- Databricks/Delta Lake; orchestration and notebook-based development.
- Visualization tools (Streamlit/Shiny/PowerBI/Spotfire/Tableau).
- Ontology frameworks/standard models (Allotrope, ISA-88, OPC-UA).
- DoE and CPP/CQA-focused process characterization.
Application:
- Apply via https://jobs.merck.com/us/en (deadline listed on the posting).
- Build and maintain robust, scalable ETL/ELT data pipelines ingesting upstream biologics experimental/process data.
- Deliver analysis-ready datasets for process characterization models, scale-up predictions, multivariate analytics, and high-throughput workflows.
- Map instrument outputs/results to ensure ontology alignment and interoperability.
- Develop data visualizations, dashboards, and reports for scientists.
- Support system-of-record standards for consistent data entry.
- Identify/flag data quality issues, metadata gaps, and inconsistencies; improve data capture.
- Collaborate with scientists and engineers to translate evolving data needs into pipeline requirements.
- Coordinate with adjacent engineers for seamless data handoffs.
- Maintain/version pipeline code in GitHub (code review, documentation, deployment).
Education (minimum):
- Ph.D. (CS/Data Science/Molecular Modeling/Engineering/Chemistry/Physics/Biology/Pharmaceutical Sciences or related).
- OR M.S. in related field with 2+ years industrial/pharma/relevant experience.
- OR B.S. in related field with 4+ years industrial/pharma/relevant experience.
Required skills/experience:
- Proficient in Python and/or R; comfortable with Jupyter/Posit-RStudio/VS Code.
- Solid SQL for relational databases and data warehouses.
- ETL/ELT pipeline experience in scientific/pharma context.
- Git/GitHub and collaborative development practices.
- Cross-functional teamwork; learning mindset and scientific curiosity.
Preferred skills/experience:
- Upstream biologics unit operations (cell culture, bioreactor, scale-up, media/feed optimization, harvest/clarification).
- Databricks/Delta Lake; orchestration and notebook-based development.
- Visualization tools (Streamlit/Shiny/PowerBI/Spotfire/Tableau).
- Ontology frameworks/standard models (Allotrope, ISA-88, OPC-UA).
- DoE and CPP/CQA-focused process characterization.
Application:
- Apply via https://jobs.merck.com/us/en (deadline listed on the posting).