Scientific Technical Engineer - PDS&T CMC
$96,500–$183,500 year
HybridNorth Chicago, Illinois, United States
Job Summary
Design and implement scalable data ingestion pipelines connecting CMC and manufacturing source systems—including MES, historians, LIMS, QMS, ERP, and instrument platforms—to centralized environments. Develop harmonized data models and ontologies that align source fields to enterprise standards while collaborating with scientists to ensure domain accuracy. Build automated quality controls, observability frameworks, and metadata infrastructure to support GxP compliance and 21 CFR Part 11 requirements. Architect governed data products for AI/ML consumption, including feature stores and vector layers for RAG applications, eliminating data bottlenecks through structural solutions. Contribute to cloud-based lakehouse architecture and CI/CD pipelines while maintaining uptime for production data assets. This role drives the first-in-AbbVie AI playbook for biologics, directly impacting regulatory submissions and manufacturing decisions across the enterprise-scale portfolio.
Required Qualifications
- Bachelor's Degree Computer Science, Data Engineering, Information Systems, Software Engineering, Bioinformatics, or a closely related technical field
- 6 years' experience (with Bachelor's Degree)
- 5 years' experience (with Master's Degree)
- 0 years' experience (with PhD)
- 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
- Bias for impact: you are drawn to problems where the stakes are high and the analytical opportunity is real, and you are energized rather than intimidated by ambiguity
Desired Qualifications
- 3+ years of hands-on experience designing and building enterprise-grade data pipelines, integration workflows, and data products in complex, multi-source environments
- Familiarity with technology transfer workflows, process characterization study design, or commercial process validation (PPQ/PV) in a biologics or pharmaceutical context
- Experience in pharmaceutical, biotech, or other regulated life sciences manufacturing environments
- Familiarity with GxP data principles, 21 CFR Part 11 compliance, or data integrity requirements in regulated industries
- Prior exposure to manufacturing source systems such as MES, process historians (e.g., OSIsoft PI/AVEVA), LIMS, QMS, or ERP platforms
- Experience building data infrastructure for AI/ML programs — including feature engineering pipelines, model training datasets, or vector/embedding data layers for RAG architectures
- Knowledge of biologics manufacturing processes (e.g., upstream cell culture, downstream purification, fill-finish) or CMC development workflows
- Familiarity with data mesh, data fabric, or federated data architecture patterns
- Experience with graph databases, knowledge graphs, or ontology frameworks applied to scientific or manufacturing data
- Contributions to open-source data tooling or demonstrated engagement with the modern data engineering community
- Design logical, physical, and conceptual data models using modeling tools (e.g., Erwin, PowerDesigner, dbt)
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