Scientific Technical Engineer, PDS&T CMC

What you'll do

• Design and implement scalable data ingestion pipelines connecting manufacturing systems like MES, LIMS, and ERP to centralized environments.
• Develop harmonized data models and ontologies to ensure consistency across diverse manufacturing sites and modalities.
• Implement automated data quality controls, validation frameworks, and lineage documentation to ensure GxP compliance.
• Architect and deliver versioned data products, including feature stores and vector-ready layers, for AI/ML consumption.
• Build and maintain cloud-based data platform infrastructure using infrastructure-as-code and CI/CD pipelines.
• Provide operational support for production data pipelines to maintain uptime and data freshness commitments.
• Translate model data requirements from data scientists into reliable and scalable data infrastructure.

What we're looking for

• Bachelor’s Degree in Computer Science, Data Engineering, Information Systems, Software Engineering, Bioinformatics, or a related field plus 6 years’ experience.
• Master’s Degree plus 5 years’ experience or a PhD plus 0 years’ experience.
• Hands-on experience designing and building enterprise-grade data pipelines, integration workflows, and data products in complex, multi-source environments.
• Expert-level proficiency in Python for data engineering tasks, including pipeline development, transformation logic, and automation.
• Strong SQL skills across modern analytical and transactional databases, including ANSI SQL and platform-specific dialects.
• Demonstrated experience with cloud data platforms (AWS, Azure, or GCP) and tools such as dbt, Spark, Airflow, Databricks, or Snowflake.
• Experience developing ETL/ELT pipelines using tools such as Informatica, Talend, Apache NiFi, or cloud-native services like AWS Glue or Azure Data Factory.
• Experience in pharmaceutical, biotech, or regulated life sciences manufacturing environments (preferred).