• Design, build, and operate automated data pipelines integrating distributed file-based data stores and raw operational system databases, including historians (PI), MES, ELN and instrument data, into a coherent, end-to-end operational data foundation. 
• Replace manual, file-based data handling with governed, versioned and traceable data flows that stand up to daily operational use. 
• Implement and enforce data quality checks, validation rules and monitoring to ensure data reliability at scale in an operational environment. 
• Deliver analytics-ready datasets (clean schemas, consistent semantics, time-aligned data) to downstream BI, advanced analytics and modeling layers supporting operational decision-making. 
• Work closely with MSAT, Process Development, Automation, QA, IT and global data teams to align data models, definitions and integration patterns across the organization. 
• Continuously improve pipeline performance, robustness and maintainability in line with operational priorities. 

What we are looking for:

• Proven experience building, operating, and optimizing production-grade data pipelines in manufacturing, industrial, or operational environments.
• Strong SQL expertise with the ability to design efficient data models and optimize queries for high-volume operational workloads.
• Hands-on experience with ETL/ELT pipeline development and integration of process, execution, and operational data sources.
• Practical knowledge of OT systems, including MES, PI Historian, instrument data, or similar industrial data platforms.
• Solid understanding of data quality, lineage, traceability, governance, and compliance, ideally within GxP-regulated environments.
• Experience with Azure data services, including Azure Data Factory, Azure Data Lake/Blob Storage, Git-based version control, and cloud-native data architectures.
• Background in biopharma, life sciences, manufacturing, or operations data, with the ability to translate complex operational processes into scalable data solutions.