This role pays less than
87%
of similar roles. Most pay
$126,800–$211,200
— the shaded band above.
At the midpoint, this role pays about
$123k
versus about
$169k
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
340 open roles
on FindRole.
Listed pay typically runs
$109,500–$208,500
across 310 roles with salary data.
The Data Engineer, AI Enablement joins the DELOS team within the Information Research organization to build reliable data foundations for R&D. This role focuses on designing, developing, and operating scalable data pipelines and curated products that make high-value research data accessible for analytics, machine learning, and knowledge graph capabilities. You will perform data curation, normalization, modeling, and metadata management to prepare assets for AI-ready use cases, including retrieval-augmented generation and vector database workflows. The role requires proficiency in SQL, Python, ETL/ELT patterns, and Airflow, alongside experience with cloud infrastructure, lakehouse patterns, and distributed SQL environments. You will collaborate with cross-functional teams to translate scientific requirements into technical solutions while providing guidance to contracted engineers. This work addresses the challenge of creating trusted, governed data products to support critical scientific decision-making in research environments.
What does a Data Engineer earn?
Median $177300 from 230 postings across 57 companies.
Design, build, and operate scalable data pipelines to create curated, AI-ready data products for R&D use.
Integrate and normalize data from various sources including databases, APIs, and external systems into the ARCH environment.
Prepare data for knowledge graphs by performing cleaning, labeling, metadata organization, and embedding workflows.
Establish robust data foundations by ensuring assets are structured, documented, governed, and traceable for downstream consumption.
Translate scientific and business requirements from stakeholders into technical specifications for data products.
Provide technical guidance to contracted engineers by reviewing outputs and ensuring they meet quality standards.
Monitor pipeline performance and resolve issues related to data freshness, cost, and system failures.
Ensure all data activities comply with GxP regulations, security policies, and corporate governance standards.
What we're looking for
Bachelor’s Degree with 5 years of experience or Master’s Degree with 4 years of experience in IT, data engineering, life sciences, or a related field.
Hands-on experience designing and operating production data pipelines using SQL, Python, ETL/ELT patterns, and workflow orchestration tools like Airflow.
Working knowledge of modern data platforms, cloud infrastructure, data warehousing, lakehouse patterns, and distributed SQL or big data environments.
Experience preparing data for machine learning, knowledge graphs, and retrieval use cases including cleaning, metadata organization, and vector-search workflows.
Experience applying data quality, governance, lineage, documentation, and modeling practices to support trusted, reusable data products.
Ability to provide technical guidance, clarify requirements, and review outputs from contracted or managed-services engineers without direct reporting authority.
Strong communication skills to translate complex technical concepts for scientific, business, and technical stakeholders.
Pharmaceutical/healthcare experience, knowledge of graph databases, and familiarity with tools like Databricks, Spark, Snowflake, or Neo4j (preferred).