Associate Scientific Technical Engineer II, PDS&T CMC
AbbVie
August 17, 2026
Remote friendly (Waltham, MA)
United States
$65,500 - $125,500 USD yearly
Operations
Job Description (AI-native Data Engineering in CMC/Manufacturing)
Responsibilities:
- Design and implement scalable data ingestion pipelines connecting MES, historians, LIMS, QMS, ERP, and instrument sources to centralized/federated environments.
- Build integration layers for heterogeneous formats/protocols and support batch and real-time/streaming patterns.
- Develop harmonized CMC/manufacturing data models and ontologies; execute semantic mapping for fields/units/identifiers.
- Implement automated data quality controls, validation, anomaly detection; establish observability (monitoring/alerting/SLA).
- Maintain lineage/metadata for traceability and support GxP-aligned governance (incl. 21 CFR Part 11, AbbVie standards).
- Architect governed, versioned, reusable data products for AI/ML (feature stores, curated datasets, vector/RAG/LLM-ready layers).
- Contribute to cloud lakehouse platform evolution (catalog, access control, compute); build IaC, CI/CD, automated tests; support onboarding and production uptime.
- Influence technical decisions through scientific rigor.
Qualifications:
Required:
- Bachelor’s (CS/Data Eng/IS/SE/Bioinformatics or related) + 2 years OR Master’s + 0 years.
- Enterprise-grade data pipeline/integration/data product experience in multi-source environments.
- Expert Python; strong SQL (ANSI + dialects).
- Cloud data platforms (AWS/Azure/GCP) + modern stack (dbt/Spark/Airflow/Databricks/Snowflake or equivalents).
- ETL/ELT (Informatica/Talend/NiFi; AWS Glue/Azure Data Factory).
- MDM/metadata/catalog for lineage/compliance; API/data integration standards (REST/GraphQL/OData) and data modeling tools (Erwin/PowerDesigner/dbt).
- Ownership, solution-architect thinking, scientific integrity, credibility, bias for impact.
Preferred:
- Regulated life sciences manufacturing; GxP/21 CFR Part 11/data integrity familiarity.
- Exposure to MES/historians (OSIsoft PI/AVEVA)/LIMS/QMS/ERP.
- AI/ML data infrastructure (feature engineering, training datasets, RAG vector/embedding layers).
Benefits (explicitly stated):
- Paid time off; medical/dental/vision insurance; 401(k); short-term incentive program eligibility.
Responsibilities:
- Design and implement scalable data ingestion pipelines connecting MES, historians, LIMS, QMS, ERP, and instrument sources to centralized/federated environments.
- Build integration layers for heterogeneous formats/protocols and support batch and real-time/streaming patterns.
- Develop harmonized CMC/manufacturing data models and ontologies; execute semantic mapping for fields/units/identifiers.
- Implement automated data quality controls, validation, anomaly detection; establish observability (monitoring/alerting/SLA).
- Maintain lineage/metadata for traceability and support GxP-aligned governance (incl. 21 CFR Part 11, AbbVie standards).
- Architect governed, versioned, reusable data products for AI/ML (feature stores, curated datasets, vector/RAG/LLM-ready layers).
- Contribute to cloud lakehouse platform evolution (catalog, access control, compute); build IaC, CI/CD, automated tests; support onboarding and production uptime.
- Influence technical decisions through scientific rigor.
Qualifications:
Required:
- Bachelor’s (CS/Data Eng/IS/SE/Bioinformatics or related) + 2 years OR Master’s + 0 years.
- Enterprise-grade data pipeline/integration/data product experience in multi-source environments.
- Expert Python; strong SQL (ANSI + dialects).
- Cloud data platforms (AWS/Azure/GCP) + modern stack (dbt/Spark/Airflow/Databricks/Snowflake or equivalents).
- ETL/ELT (Informatica/Talend/NiFi; AWS Glue/Azure Data Factory).
- MDM/metadata/catalog for lineage/compliance; API/data integration standards (REST/GraphQL/OData) and data modeling tools (Erwin/PowerDesigner/dbt).
- Ownership, solution-architect thinking, scientific integrity, credibility, bias for impact.
Preferred:
- Regulated life sciences manufacturing; GxP/21 CFR Part 11/data integrity familiarity.
- Exposure to MES/historians (OSIsoft PI/AVEVA)/LIMS/QMS/ERP.
- AI/ML data infrastructure (feature engineering, training datasets, RAG vector/embedding layers).
Benefits (explicitly stated):
- Paid time off; medical/dental/vision insurance; 401(k); short-term incentive program eligibility.