Data Engineer – Process Analytics & Data Intelligence

About Lonza

At Lonza, our people are our greatest strength. With 30+ sites across five continents, our globally connected teams work together every day to manufacture the medicines of tomorrow. Our core values of Collaboration, Accountability, Excellence, Passion and Integrity reflect who we are and how we work together. Everyone’s ideas, big or small, have the potential to improve millions of lives, and that’s the kind of work we want you to be part of. Innovation thrives when people from all backgrounds bring their unique perspectives to the table. At Lonza, we value diversity and are committed to creating an inclusive environment for all employees. If you’re ready to help turn our customers’ breakthrough ideas into viable therapies, we look forward to welcoming you on board.

What You Will Do

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.

Fähigkeiten

MySQLICTSQLGit

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