Associate Scientific Technical Engineer II, PDS&T CMC

AbbVie discovers and delivers innovative medicines and solutions addressing serious health issues across areas including immunology, oncology, and neuroscience. The Associate Scientific Technical Engineer II will design, build, and operate production-grade data pipelines and data products supporting AI/ML, analytics, and automation across CMC and manufacturing. The role integrates and harmonizes data from manufacturing and development systems while enabling governed, reliable data assets for scientists, engineers, and AI systems.

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

Support both batch and real-time/streaming data patterns, selecting appropriate architectures based on use case requirements

Develop and maintain harmonized data models and ontologies that bring consistency to CMC and manufacturing data across sites, systems, and modalities

Execute semantic mapping efforts that align source system fields, units, and identifiers to enterprise data standards and scientific meaning

Collaborate with process scientists, analytical chemists, and manufacturing engineers to ensure data models accurately reflect domain reality

Implement automated data quality controls, validation frameworks, and anomaly detection mechanisms across pipeline layers

Build and maintain data lineage documentation and metadata infrastructure, enabling full traceability from source system to AI model input

Establish pipeline observability practices — monitoring, alerting, SLA tracking — to ensure data product reliability in production

Support data governance practices aligned with GxP requirements, 21 CFR Part 11, and AbbVie data standards

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

Partner closely with data scientists, ML engineers, and process modelers to understand model data requirements and translate them into reliable, scalable data infrastructure

Accelerate AI program delivery by eliminating data bottlenecks — not by workarounds, but by solving root causes structurally

Contribute to the design and evolution of PDST's cloud-based data platform, including lakehouse architecture, data cataloging, access control, and compute infrastructure

Write and maintain infrastructure-as-code, CI/CD pipelines, and automated testing frameworks for data systems

Support platform onboarding of new CMC data domains and manufacturing sites, ensuring consistent application of standards and patterns

Provide operational support for production data pipelines, maintaining uptime and data freshness commitments

Influence technical decision-making without formal authority — earning trust through scientific rigor, transparent methodology, and demonstrated business impact

Preferred

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

Regulated life sciences manufacturing

GxP data principles

21 CFR Part 11 compliance

Regulated data integrity

MES

OSIsoft PI/AVEVA

LIMS

QMS

ERP platforms

AI/ML data infrastructure

Feature engineering pipelines

Model training datasets

Vector

embedding data layers

RAG architectures

Benefits

Paid time off (vacation, holidays, sick)

Medical, dental, and vision insurance for eligible employees

401(k) for eligible employees

Eligibility to participate in short-term incentive programs

Company

Global biopharmaceutical company developing medicines for immunology, oncology, neuroscience, aesthetics.

North Chicago, Illinois, USA

https://www.abbvie.com/

Founded in 2013

10001+ employees

Qualifications

Bachelor's Degree in Computer Science, Data Engineering, Information Systems, Software Engineering, Bioinformatics, or a closely related technical field plus 2 years' experience OR Master's Degree with 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

Design logical, physical, and conceptual data models using modeling tools (e.g., Erwin, PowerDesigner, dbt)

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

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

Bachelor's Degree in Computer Science, Data Engineering, Information Systems, Software Engineering, Bioinformatics, or a closely related technical field plus 2 years’ experience OR Master’s Degree with 0 years' experience.

Required skills

Data Pipeline Engineering, Python, SQL, Cloud Data Platforms, ETL/ELT, Databricks, Apache Spark, Apache Airflow, Snowflake, Master Data Management, Data Modeling, API Integration REST, API Integration GraphQL, API Integration OData

Experience

2+ years