Engineer, Technology I

AbbVie•Mettawa, IL

5d•$75,000 - $142,500•Hybrid

About The Position

AbbVie’s Business Technology Solutions (BTS) Information Research organization is seeking an Engineer, Technology I to deliver trusted, well-structured, AI-ready data products within ARCH, AbbVie’s R&D Convergence Hub. As part of the DELOS team (Data Exploration and Linked Outcome Solutions), this role builds reliable data foundations for analytics, reporting, knowledge graph capabilities, machine learning, and AI-enabled use cases across R&D. The Engineer independently designs and develops data pipelines and curated data assets for assigned projects using established architecture, engineering patterns, and governance standards. The work includes collecting, integrating, normalizing, modeling, transforming, cleaning, standardizing, enriching, and documenting research data; implementing quality checks; and preparing assets for publication to the ARCH knowledge graph. The role prepares reliable, accessible, appropriately governed data for downstream analytics, data science, and AI-enabled use cases rather than developing AI models directly. Working with R&D stakeholders, data scientists, machine learning engineers, platform teams, architects, data owners, and other contributors, the Engineer translates defined scientific and business requirements into dependable data solutions. The role performs routine and complex engineering tasks independently, manages assigned work within agreed scope and timelines, adapts to changing requirements or priorities, communicates progress and risks, troubleshoots issues within its scope, and escalates broader issues as needed. The role reports under the direction of the Associate Director – Data Strategy, AI & Knowledge Enablement. This is a hybrid role based in Lake County, Illinois, with onsite work Tuesday, Wednesday, and Thursday. Travel is 5% of the time.

Requirements

Bachelor’s degree in Information Technology, Data Engineering, Computer Science, Informatics, Life Sciences, or a related field plus 4 years of experience; alternatively, a master’s degree plus 2 years of experience.
Practical experience developing data pipelines or data solutions using SQL, Python, and ETL/ELT patterns.
Knowledge of workflow orchestration tools such as Airflow.
Theoretical and practical knowledge of data integration, data modeling, data storage, and modern data platforms sufficient to independently perform routine and complex engineering tasks within established standards.
Experience cleaning, standardizing, transforming, and documenting data for analytics or scientific use, including applying data quality checks and organizing metadata.
Ability to organize assigned work, multitask, meet timelines, understand project objectives, adapt approaches when needed, and learn and apply new methods and technologies.
Strong written and oral English communication skills, including the ability to explain technical work, present project objectives and progress, and collaborate with scientific, business, and technical partners.
Ability to develop reusable, well-structured data assets for defined analytics and AI-enabled use cases.
Ability to apply established metadata, lineage, governance, and appropriate-use practices to assigned data assets.
Practical technical problem-solving skills with data platforms, pipelines, integration, and orchestration, and ability to learn and implement new tools and methods.
Ability to monitor pipelines, troubleshoot issues, meet timelines, and escalate risks when needed.
Ability to clarify requirements, share technical knowledge, and collaborate effectively with internal and external contributors.

Nice To Haves

Pharmaceutical, healthcare, or life sciences industry experience, particularly with research, discovery, translational, clinical, or scientific data.
Familiarity with knowledge graphs, graph databases, ontology-based structures, semantic data, or linked-data concepts.
Exposure to cloud or modern data platform technologies such as AWS, Databricks, Spark, Snowflake, or Neo4j.
Exposure to Agile delivery practices or tools such as Jira.
Exposure to data preparation for machine learning, retrieval-augmented generation (RAG), embeddings, or vector search.
Familiarity with governance, security, privacy, licensing, or documentation in regulated data environments.

Responsibilities

Independently design and develop data pipelines and curated data assets for assigned scientific or business use cases.
Collect, integrate, normalize, model, and transform data from databases, applications, APIs, licensed external sources, and other systems using established engineering patterns.
Clean, standardize, enrich, and structure data and documents for analytics, machine learning, retrieval-augmented generation (RAG), and knowledge discovery.
Organize metadata, support established chunking and embedding workflows, and prepare curated assets for publication to the ARCH knowledge graph.
Implement defined data quality checks and maintain technical documentation covering metadata, lineage, transformations, assumptions, and usage guidance.
Apply established access, privacy, licensing, and governance requirements to data assets.
Organize assigned work, understand project objectives, adapt to changing requirements or priorities, and deliver reliable results within agreed timelines.
Work with scientific, business, engineering, and platform partners to clarify requirements and deliver assigned work.
Communicate project objectives, progress, risks, and issues; collaborate with internal and contracted contributors on assigned tasks.
Monitor assigned pipelines for performance, freshness, failures, and resource use; troubleshoot and resolve issues within scope and escalate when needed.
Learn and implement new methods and technologies and contribute to reusable patterns and automation with guidance as appropriate.
Adhere to applicable Corporate and Divisional policies, including code of conduct, safety, GxP compliance, data security, data governance, regulatory or contractual requirements, and the software development lifecycle.