Associate Scientific Technical Engineer II, PDS&T CMC

AbbVie

Confirmed live today Trusted

Quick summary

Work type: On-site
Location: Worcester, MA
Salary: $65,500–$125,500 / yr
Posted: 71 days ago
Freshness: Confirmed live today
Closes: Aug 17, 2126
Nearby: 99+ roles within 25 mi

Market check

Salary context

Below market

How this pay compares to similar roles

Similar $185k

This role $96k

$46k $244k

below market most similar roles pay here above market

This role pays less than 95% of similar roles. Most pay $152,150–$217,500 — the blue band above. At the midpoint, this role pays about $96k versus about $185k 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 145 open roles on FindRole.

Listed pay typically runs $109,500–$208,500 across 140 roles with salary data.

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View all roles at AbbVie

At a glance

TL;DR · Associate Scientific Technical Engineer II, PDS&T CMC

Role Posting Log in to save

The Associate Scientific Technical Engineer II, PDS&T CMC joins the BTS - PDST team to build an AI-native data infrastructure for pharmaceutical manufacturing and product development. This role involves designing and operating production-grade data pipelines that integrate heterogeneous data from MES, historians, LIMS, QMS, ERP, and instrument platforms into governed, semantically rich assets. You will develop harmonized data models, implement automated quality controls, and architect reusable data products like feature stores and vector-ready layers for RAG and LLM applications. Key technologies include Python, SQL, AWS, Azure, GCP, dbt, Spark, Airflow, Databricks, Snowflake, and Informatica. The work addresses the complexity of biological systems and regulatory expectations by creating a scalable AI playbook to accelerate end-to-end product development and maximize yields for a biologics portfolio.

Skills

Python SQL AWS Azure GCP Databricks Snowflake Spark Airflow dbt CI/CD ETL ELT REST GraphQL OData Microservices Informatica Talend Apache NiFi RAG LLM Lakehouse Infrastructure-as-Code MES LIMS QMS ERP OSIsoft PI Erwin PowerDesigner

What you'll do

Design and implement scalable data ingestion pipelines from manufacturing systems like MES, LIMS, and ERP.
Build connectors and integration layers to handle heterogeneous data formats and protocols in pharmaceutical environments.
Develop harmonized data models and ontologies to ensure consistency across different manufacturing sites and modalities.
Implement automated data quality controls, validation frameworks, and lineage documentation to meet GxP requirements.
Architect and deliver versioned data products, including feature stores and vector-ready layers for AI/ML applications.
Write and maintain infrastructure-as-code, CI/CD pipelines, and automated testing frameworks for data systems.
Provide operational support for production data pipelines to maintain uptime and data freshness commitments.

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’ experience.
Master’s Degree with 0 years' experience.
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, including ANSI SQL and platform-specific dialects.
Experience with cloud data platforms (AWS, Azure, or GCP) and tools such as dbt, Spark, Airflow, Databricks, Snowflake, or equivalents.
Experience developing ETL/ELT pipelines using tools such as Informatica, Talend, Apache NiFi, and cloud-native services.
Experience in pharmaceutical, biotech, or other regulated life sciences manufacturing environments (preferred).

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