Scientific Technical Engineer - PDS&T CMC
AbbVie
August 14, 2026
Remote friendly (North Chicago, IL)
United States
$96,500 - $183,500 USD yearly
Operations
Responsibilities:
- Design, build, and operate scalable, robust data ingestion pipelines connecting CMC/manufacturing source systems (MES, historians, LIMS, QMS, ERP, instruments) to centralized/federated environments.
- Build connectors/adapters/integration layers for heterogeneous formats, protocols, and latency; support batch and real-time/streaming patterns.
- Develop and maintain harmonized data models and ontologies; perform semantic mapping of fields/units/identifiers to enterprise standards.
- Implement data quality controls, validation, anomaly detection, and pipeline observability (monitoring, alerting, SLA tracking).
- Build and maintain data lineage/metadata infrastructure; support governance aligned with GxP/21 CFR Part 11.
- Architect governed, versioned, reusable data products for AI/ML (feature stores, curated datasets, vector-ready layers for RAG/LLMs).
- Contribute to cloud platform evolution (lakehouse, cataloging, access control, compute); implement IaC, CI/CD, automated testing; support onboarding and production operations.
- Influence technical decisions through scientific rigor and demonstrated business impact.
Qualifications:
Required:
- BS (CS/Data Eng/IS/Software Eng/Bioinformatics or related) +6 yrs; MS +5 yrs; PhD +0.
- Hands-on enterprise data pipelines/data products in complex multi-source environments.
- Expert Python; strong SQL (ANSI + dialects).
- Cloud platforms (AWS/Azure/GCP) and modern stack (dbt, Spark, Airflow, Databricks, Snowflake).
- ETL/ELT (Informatica, Talend, NiFi, AWS Glue, Azure Data Factory).
- MDM/metadata/data cataloging; API standards (REST/GraphQL/OData) and microservices.
- Ownership, solution-architect mindset, scientific integrity, credibility, bias for impact.
Preferred:
- 3+ yrs similar experience; regulated life sciences/manufacturing exposure.
- GxP/21 CFR Part 11/data integrity; experience with MES/historians/LIMS/QMS/ERP.
- AI/ML data infrastructure (feature engineering, training datasets, RAG embeddings/vector layers); biologics/CMC familiarity.
- Data mesh/fabric/federated patterns; graph databases/knowledge graphs/ontologies; open-source contributions; data modeling tools (Erwin, PowerDesigner, dbt).
Benefits (explicitly stated):
- Paid time off; medical/dental/vision insurance; 401(k) (eligible employees); short-term incentive program eligible.
- Design, build, and operate scalable, robust data ingestion pipelines connecting CMC/manufacturing source systems (MES, historians, LIMS, QMS, ERP, instruments) to centralized/federated environments.
- Build connectors/adapters/integration layers for heterogeneous formats, protocols, and latency; support batch and real-time/streaming patterns.
- Develop and maintain harmonized data models and ontologies; perform semantic mapping of fields/units/identifiers to enterprise standards.
- Implement data quality controls, validation, anomaly detection, and pipeline observability (monitoring, alerting, SLA tracking).
- Build and maintain data lineage/metadata infrastructure; support governance aligned with GxP/21 CFR Part 11.
- Architect governed, versioned, reusable data products for AI/ML (feature stores, curated datasets, vector-ready layers for RAG/LLMs).
- Contribute to cloud platform evolution (lakehouse, cataloging, access control, compute); implement IaC, CI/CD, automated testing; support onboarding and production operations.
- Influence technical decisions through scientific rigor and demonstrated business impact.
Qualifications:
Required:
- BS (CS/Data Eng/IS/Software Eng/Bioinformatics or related) +6 yrs; MS +5 yrs; PhD +0.
- Hands-on enterprise data pipelines/data products in complex multi-source environments.
- Expert Python; strong SQL (ANSI + dialects).
- Cloud platforms (AWS/Azure/GCP) and modern stack (dbt, Spark, Airflow, Databricks, Snowflake).
- ETL/ELT (Informatica, Talend, NiFi, AWS Glue, Azure Data Factory).
- MDM/metadata/data cataloging; API standards (REST/GraphQL/OData) and microservices.
- Ownership, solution-architect mindset, scientific integrity, credibility, bias for impact.
Preferred:
- 3+ yrs similar experience; regulated life sciences/manufacturing exposure.
- GxP/21 CFR Part 11/data integrity; experience with MES/historians/LIMS/QMS/ERP.
- AI/ML data infrastructure (feature engineering, training datasets, RAG embeddings/vector layers); biologics/CMC familiarity.
- Data mesh/fabric/federated patterns; graph databases/knowledge graphs/ontologies; open-source contributions; data modeling tools (Erwin, PowerDesigner, dbt).
Benefits (explicitly stated):
- Paid time off; medical/dental/vision insurance; 401(k) (eligible employees); short-term incentive program eligible.