This role pays less than
92%
of similar roles. Most pay
$131,812–$208,800
— the shaded band above.
At the midpoint, this role pays about
$96k
versus about
$170k
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.
Associate Scientific Technical Engineer II, PDS&T CMC joins the BTS - PDST team as an AI-native engineer focused on pharmaceutical data engineering. This role involves designing and operating production-grade data pipelines to power AI, machine learning, and automation across the manufacturing and development ecosystem. You will integrate and harmonize complex data from systems like MES, LIMS, QMS, and ERP platforms into semantically rich assets for scientific use. Key responsibilities include developing robust ingestion pipelines, creating harmonized data models, ensuring GxP compliance, and building 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 critical bottleneck of utilizing complex biological data to accelerate product development and manufacturing decisions in a regulated environment.
Design and implement scalable data ingestion pipelines from manufacturing systems like MES, LIMS, and ERP platforms into centralized environments.
Build connectors and integration layers to handle diverse data formats and protocols in pharmaceutical manufacturing settings.
Develop harmonized data models and ontologies to ensure consistency across different sites and production modalities.
Implement automated quality controls, lineage documentation, and observability metrics to ensure data reliability and GxP compliance.
Architect and deliver governed data products, such as feature stores and vector layers, specifically for AI/ML consumption.
Develop infrastructure-as-code, CI/CD pipelines, and automated testing frameworks for the cloud-based data platform.
Collaborate with scientists and engineers to translate complex manufacturing requirements into scalable technical data architectures.
What we're looking for
Bachelor's Degree in Computer Science, Data Engineering, Information Systems, Software Engineering, Bioinformatics, or a related technical field plus 2 years of experience, or a Master's Degree.
Experience designing and building enterprise-grade data pipelines, integration workflows, and data products in 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 like dbt, Spark, Airflow, Databricks, or Snowflake.
Ability to develop ETL/ELT pipelines using tools such as Informatica, Talend, Apache NiFi, or cloud-native services.
Proficiency in master data management, metadata management, data cataloging, and API development (REST, GraphQL, OData).
Experience in pharmaceutical manufacturing, GxP compliance, or building infrastructure for AI/ML programs is preferred.
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