Biopharma Data Engineer: AI/ML Data Pipelines

Allergan; North Chicago (IL); On-site; USD 130,000 - 190,000; Full time

Job summary

AbbVie is seeking an AI-native Data Engineer within PDST to design, build, and operate production-grade data pipelines powering AI/ML for CMC and manufacturing. You will integrate data from MES, LIMS, QMS, ERP and instrument platforms, transforming it into governed data assets for scientists and AI systems.

Responsibilities include data ingestion, harmonization, quality, observability, and delivery of data products while aligning with GxP and regulatory standards.

Qualifications

• Bachelor's or Master’s degree with hands-on data engineering experience.
• Proficiency in Python, SQL, and cloud data platforms.
• Experience building scalable data pipelines and data products for AI/ML.
• Knowledge of data governance, lineage, and semantic modeling.
• Experience with modern data stack tools and API development.

Responsibilities

• Design and build scalable data ingestion pipelines across multiple source systems.
• Develop harmonized data models and semantic mappings for enterprise use.
• Implement data quality, observability, and governance across pipelines.
• Deliver governed data products and feature stores for AI/ML workloads.
• Contribute to cloud data platform design and CI/CD for data systems.

Skills

Python

SQL

Cloud platforms

Data pipelines

ETL/ELT

Data modeling

MDM

APIs (REST/GraphQL/OData)

Spark

Airflow

Education

Bachelor's Degree in Computer Science, Data Engineering, Information Systems, or related field

Master’s Degree (preferred)

Tools

dbt

Spark

Airflow

Databricks

Snowflake

Informatica

Talend

Apache NiFi

OSISoft PI / AVEVA

Erwin / PowerDesigner

Job description

AbbVie is seeking an AI-native Data Engineer within PDST to design, build, and operate production-grade data pipelines powering AI/ML for CMC and manufacturing. You will integrate data from MES, LIMS, QMS, ERP and instrument platforms, transforming it into governed data assets for scientists and AI systems.

Responsibilities include data ingestion, harmonization, quality, observability, and delivery of data products while aligning with GxP and regulatory standards.