This role pays less than
92%
of similar roles. Most pay
$131,062–$208,200
— 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 and products to power AI/ML, analytics, and automation across the manufacturing ecosystem. You will integrate and harmonize complex data from MES, historians, LIMS, QMS, ERP, and instrument platforms into semantically rich assets for scientists and engineers. Key responsibilities include building ETL/ELT pipelines, developing harmonized data models, ensuring GxP compliance, 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, Databricks, Snowflake, Informatica, and Talend. The role addresses the challenge of accelerating drug development and manufacturing yields through advanced data engineering in a regulated environment.
Design and implement scalable data ingestion pipelines from manufacturing systems like MES, LIMS, and ERP platforms.
Develop harmonized data models and ontologies to ensure consistency across diverse manufacturing sites and modalities.
Implement automated data quality controls, lineage documentation, and observability for production-grade data products.
Build and maintain feature stores and curated datasets specifically designed for AI/ML consumption and RAG applications.
Manage infrastructure-as-code, CI/CD pipelines, and automated testing frameworks for the cloud-based data platform.
Ensure all data systems comply with GxP requirements and 21 CFR Part 11 regulations.
Collaborate with scientists and engineers to translate complex manufacturing requirements into reliable 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 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 with 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.
MongoDB
Cloud Manager
Distributed Systems
Linux
Windows
AWS
Azure
Google Cloud Platform
Python
Java
Go
Ruby
C++
JavaScript
Node.js
Network Administration
Performance Tuning
Benchmarking