Scientific Technical Engineer - PDS&T CMC

Principal Data Engineer

Abb Vie's mission is to discover and deliver innovative medicines and solutions that solve serious health issues today and address the medical challenges of tomorrow. We strive to have a remarkable impact on people's lives across several key therapeutic areas including immunology, oncology and neuroscience - and products and services in our Allergan Aesthetics portfolio. For more information about Abb Vie, please visit us at

This role is embedded inside PDST and works at the frontier of pharmaceutical data engineering. You will integrate and harmonize data from the full spectrum of manufacturing and development systems — including MES, historians, LIMS, QMS, ERP, and instrument platforms — and transform it into reliable, governed, semantically rich data assets that data scientists, process engineers, and AI systems can actually use.

Responsibilities

• Design and implement scalable, robust data ingestion pipelines that connect CMC and manufacturing source systems — including MES (Manufacturing Execution Systems), process historians, LIMS, QMS, ERP platforms, and instrument data sources — to centralized and federated data environments.
• Build connectors, adapters, and integration layers that handle the heterogeneous data formats, protocols, and latency profiles characteristic of pharmaceutical manufacturing environments.
• Develop and maintain harmonized data models and ontologies that bring consistency to CMC and manufacturing data across sites, systems, and modalities.
• Implement automated data quality controls, validation frameworks, and anomaly detection mechanisms across pipeline layers.
• Architect and deliver governed, versioned, reusable data products purpose-built for AI/ML consumption, including feature stores, curated datasets, and vector-ready data layers for RAG and LLM applications.
• Contribute to the design and evolution of PDST's cloud-based data platform, including lakehouse architecture, data cataloging, access control, and compute infrastructure.
• Influence technical decision-making without formal authority — earning trust through scientific rigor, transparent methodology, and demonstrated business impact.

Qualifications

• Bachelor's Degree Computer Science, Data Engineering, Information Systems, Software Engineering, Bioinformatics, or a closely related technical field plus 6 years' experience;
  Master's Degree plus 5 years' experience;
  PhD plus 0 years' experience.
• Respective years of 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 — pipeline development, transformation logic, data validation, and automation.
• Strong SQL skills across modern analytical and transactional databases; comfort with both ANSI SQL and platform-specific dialects.
• Demonstrated experience with cloud data platforms (AWS, Azure, or GCP) and modern data stack components — including tools such as dbt, Spark, Airflow, Databricks, Snowflake, or equivalents.
• Develop ETL/ELT pipelines using tools such as Informatica, Talend, Apache NiFi, and cloud-native services (e.g., AWS Glue, Azure Data Factory).
• Implement master data management (MDM), metadata management, and data cataloging solutions to ensure proper data lineage, accessibility, and compliance.
• Set and enforce standards for API development and data integration (REST, GraphQL, OData), enabling seamless integration using microservices architectures.
• Ownership orientation: you define your own problem space, drive solutions to completion, and hold yourself accountable to outcomes — not just outputs.
• Solution-architect instinct: you think before you build, consider the full landscape of available approaches, and choose tools based on fit-for-purpose reasoning rather than familiarity or trend.
• Scientific integrity: you build models you can explain, defend, and improve — and you apply the same standard to the work of others.
• Influence through credibility: you earn the confidence of scientists, engineers, and quality professionals by being right, being clear, and being useful — not by title or volume.
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