This role pays less than
78%
of similar roles. Most pay
$150,000–$216,250
— the shaded band above.
At the midpoint, this role pays about
$140k
versus about
$183k
for comparable roles.
Based on 240 similar postings.
Employer
About AbbVie
AbbVie is a global biopharmaceutical company focused on discovering and delivering innovative medicines and solutions in immunology, oncology, neuroscience, and eye care. Its products include Humira, Skyrizi, and Rinvoq.
AbbVie currently has
297 open roles
on FindRole.
Listed pay typically runs
$109,500–$208,500
across 271 roles with salary data.
The Scientific Technical Engineer - PDS&T CMC joins the BTS-PDST team to build an AI-native infrastructure for pharmaceutical manufacturing and development. This role focuses on designing, building, and operating production-grade data pipelines and products that power AI/ML, analytics, and automation across the CMC ecosystem. The engineer will integrate and harmonize heterogeneous data from systems such as MES, LIMS, QMS, ERP, and process historians into governed, semantically rich assets. Key responsibilities include developing data models, implementing automated quality controls, and creating feature stores for RAG and LLM applications. Required skills include expert-level Python, SQL, and experience with cloud platforms like AWS, Azure, or GCP, alongside tools such as dbt, Spark, Airflow, and Databricks. The role addresses the complexity of biological systems to accelerate product development while ensuring compliance with GxP and 21 CFR Part 11 standards.
Design and implement scalable data ingestion pipelines from manufacturing systems like MES, LIMS, and ERP platforms into centralized environments.
Develop harmonized data models and ontologies to ensure consistency across diverse CMC and manufacturing data sources.
Implement automated data quality controls, validation frameworks, and monitoring tools to ensure production reliability.
Build governed, versioned data products such as feature stores and vector-ready layers for AI/ML and LLM applications.
Maintain infrastructure-as-code, CI/CD pipelines, and automated testing frameworks for cloud-based data platforms.
Ensure all data systems comply with GxP requirements and 21 CFR Part 11 regulations.
Translate complex scientific and manufacturing requirements into scalable technical architectures for data scientists and engineers.
What we're looking for
Bachelor’s degree in Computer Science, Data Engineering, Information Systems, Software Engineering, Bioinformatics, or a related technical field plus 6 years of experience; Master’s plus 5 years; PhD plus 0 years.
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 including pipeline development, transformation logic, and automation.
Strong SQL skills across modern analytical and transactional databases using various platform-specific dialects.
Experience with cloud data platforms (AWS, Azure, or GCP) and tools such as dbt, Spark, Airflow, Databricks, or Snowflake.
Proficiency in developing ETL/ELT pipelines using Informatica, Talend, Apache NiFi, or cloud-native services like AWS Glue or Azure Data Factory.
Experience implementing master data management, metadata management, and data cataloging solutions to ensure lineage and compliance.
Ability to set and enforce standards for API development (REST, GraphQL, OData) within microservices architectures.
Familiarity with pharmaceutical manufacturing systems (MES, LIMS, QMS, ERP), GxP principles, or 21 CFR Part 11 compliance (preferred).
Experience building data infrastructure for AI/ML programs including feature stores and vector-ready layers for RAG and LLM applications (preferred).
Knowledge of biologics manufacturing processes, data mesh architectures, or graph databases applied to scientific data (preferred).