> Markdown version of [/jobs/ext/2946027-data-engineer-ai-enablement](https://www.wearedevelopers.com/jobs/ext/2946027-data-engineer-ai-enablement). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Data Engineer, AI Enablement

- **Company:** AbbVie Inc.
- **Location:** North Chicago, IL, United States
- **Experience:** Experienced
- **Salary:** $84,500.0 - $162,000.0
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Agile Methodology, Artificial Intelligence, Airflow, Amazon Web Services, Data Analysis, JIRA, Big Data, Cloud Computing, Databases, Information Engineering, Data Governance, Data Infrastructure, Data Integration, Extract Transform Load (ETL), Data Security, Data Structures, Data Systems, Data Warehousing, Digital Assets, Graph Database, Python (Programming Language), Machine Learning, Metadata, Meta-Data Management, Neo4j, Scrum Methodology, Software Engineering, SQL Databases, Snowflake, Apache Spark, Information Technology, Data Management, Data Pipelines, Databricks
- **Published:** September 16, 2026
- **Apply:** https://www.biospace.com/logon?PipelinedPage=%2Fjob%2F3074669%2Fdata-engineer-ai-enablement%3FAction%3DContinueJobApplication%23application-form

## About the Role

* Bachelors Degree with 5 years of experience; ORMastersDegree with 4 years of experience in information technology, data engineering, data management, analytics, life sciences, ora relatedfield.
 * Hands-on experience designing, developing, andoperatingproduction data pipelinesandcurateddata productsusingSQL, Python, ETL/ELT patterns, and workflow orchestrationtoolssuch as Airflow.
 * Working knowledge of modern data platforms, data integration, data warehousing orlakehousepatterns, distributed SQL or big data environments, cloudinfrastructure,andanalytics enablement.
 * Experience preparing data fordownstream analytics, machine learning, knowledgegraph, or retrieval use cases, including cleaning,standardization, enrichment,structuring, metadata organization, and support for embedding or vector-search workflows.
 * Experience applying data quality, metadata management, governance, lineage, documentation, and data modeling practices to support trusted, reusable data products.
 * Experience collaborating with cross-functional business, scientific, technical, platform, vendor, contractor, or managed-services teams to translate requirementsand deliver fit-for-purpose data assets.
 * Ability tooperatewith a high degree of autonomy, manage priorities across concurrent workstreams,modifyapproach when needed, escalate open issues, and keep stakeholders informed through clear written and verbal communication.
 * Demonstrated ability to learn, understand, andapplynewdata engineering, platform,andAI-enablementtechnologies, and toserveas atechnicalresourcefor others.
 * Experienceproviding technicalinput, clarifyingrequirements,and reviewingoutputs fromcontracted, vendor, or managed-services engineers without direct reporting authority.
 * Strong communication, planning, and organizational skills, with the ability toexplaintechnicalconceptsandkeepstakeholdersinformed.
 * Dataproduct engineeringmindset, with the ability toshapereusable, well-structured data assets thatare practical, scalable, and fit foranalyticsand AI-enabled use.
 * Data curation and stewardship mindset, with attention toquality, metadata,lineage, governance, standards,documentation,andappropriate use.
 * Technical fluencyacrossdata platforms, pipelines, integration patterns, orchestration,cloudenvironments,anddata delivery practicessufficient to work effectively with engineering and platform teams.
 * Operational discipline acrossmonitoring, troubleshooting,prioritization,issue resolution,automation,reusablepatterns,and continuous improvement.
 * Technical coordination and influence, with the ability to clarifypriorities, guide work, review outputs,resolve ambiguity, and coordinate across internalandexternal contributors.
 * Stakeholder communication, with the ability toframe tradeoffs, risks, dependencies, andprogress in a clear and practical wayfor technical, scientific, and business audiences.

Preferred:

 * Pharmaceutical or healthcare industry experience preferred.
 * Experience supporting research, discovery, translational, clinical, scientific, or other life sciences data environments.
 * Familiarity with graph databases, knowledge graphs, ontology-based data structures, semantic data, metadata-driven data products, orlinked-dataconcepts.
 * Experience working with AWS-based, cloud-based,lakehouse, or modern data platform technologies such as Databricks, Spark, Snowflake, Neo4j, or similar tools.
 * Experience working with regulated data environments, including data governance, documentation, security, privacy, license terms, or compliance expectations.
 * Exposure toanalytics, machine learning, retrieval-augmented generation (RAG), embeddings, vector databases, AI-search patterns, or AI-ready data product delivery.
 * Familiarity with Agile practices or planning tools such as Jira, including backlog refinement, sprint planning, prioritization, acceptance criteria, and delivery tracking.

## Description

AbbVies Business Technology Solutions (BTS) Information Research (IR) organizationis seekingaData Engineer, AI Enablementto help deliver trusted, well-structured, AI-ready data products within ARCH, AbbVies R&D Convergence Hub.As part ofthe DELOSteam Data Exploration and Linked Outcome Solutions this role helps build the reliable data foundations needed to advance analytics, reporting, knowledge graph capabilities, machine learning, and AI-enabled use cases across R&D.

In this role, you will independently design, develop, andoperatescalable data pipelines and curated data products that make high-value research data easier to find, connect, understand, and use. The work spans data curation, normalization, modeling, metadata, lineage, quality controls, governance, documentation, and publication to the ARCH knowledge graph. Rather than developing AI models directly, you will ensure that data science, AI engineering, and research partners have the reliable, accessible, and appropriately governed data they need to deliver trusted outcomes.

Working closely with R&D stakeholders, data scientists, machine learning engineers, platform teams, architects, and data owners, you will help translate scientific and business needs into dependable data solutions. You will also help scale delivery byprovidingtechnical guidance tocontractedengineers supporting the same data products,translating requirements into clear work, reviewing outputs,helping removebarriers, and ensuring results meet agreed quality, documentation, and acceptance standards.

Under the direction of theAssociate DirectorData Strategy, AI & Knowledge Enablement, thisrole is an opportunity to contribute at the center of AbbViesR&D datatransformation.The data foundationsyou build will helpdeterminewhich analytics, knowledgegraph, and AI use cases are possible across research and howconfidentlythe organization canusetheir outputto support scientific decision-making., * AI-Ready Data Product Engineering:Design, build, andoperatecurated, reusable data products that make high-value R&D data easier to find, connect, understand, and use. Collect, integrate, normalize, model, and transform data from databases, applications, APIs, licensed external sources, and other systems into ARCH and related data environments.

 * Trusted Data Foundation Enablement:Establishreliable, scalable data foundations that support analytics, reporting, knowledge graph capabilities, machine learning, and AI-enabled use cases. Ensure data assets are structured, documented, accessible, governed, traceable, and fit for downstream consumption.
 * AI, RAG & Knowledge Graph Readiness:Prepare data and documents for AI and knowledge discovery use cases by cleaning, standardizing, enriching, labeling, organizing metadata, supportingchunking,and embedding workflows, and producing vector database-ready assets. Enable publication of curated data to the ARCH knowledge graph.
 * Data Quality, Governance & Documentation:Apply data quality and governance practices, including accuracy and completeness checks, metadata, lineage, access controls, privacy, license terms, assumptions, quality rules, and appropriate-use guidance so data consumers can understand and trust the assets they use.
 * Technical Coordination & Delivery Support:Collaborate with data scientists, machine learning engineers, software engineers, platform teams, architects, data owners, and R&D stakeholders to translate scientific and business requirements into usable AI-ready data products.Providetechnical guidance tocontractedengineers, clarify work, review outputs,helpremove barriers, andsupportdeliveryagainstagreed quality and acceptance standards.
 * Operational Reliability & Continuous Improvement:Monitor pipeline performance, data freshness, cost, failures, and delivery issues; troubleshoot andresolve problems before theyimpactdata consumers. Contribute to reusable engineering patterns, automation, process improvements, and consistent ways of working across data product workflows.
 * Compliance & Standards:Follow applicable Corporate and Divisional policies, includingGxPcompliance, data security, software development lifecycle practices, data governance standards, and relevant regulatory or contractual requirements.


## Related Videos

- [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works)
- [Putting the Graph In GraphQL With The Neo4j GraphQL Library](https://www.wearedevelopers.com/videos/257-putting-the-graph-in-graphql-with-the-neo4j-graphql-library)
- [Improving quality with Agentic AI with Rovo Dev and Xray](https://www.wearedevelopers.com/videos/2005-improving-quality-with-agentic-ai-with-rovo-dev-and-xray)
- [Enjoying SQL data pipelines with dbt](https://www.wearedevelopers.com/videos/823-enjoying-sql-data-pipelines-with-dbt)
- [AI Model Management Life Circles: ML Ops For Generative AI Models From Research to Deployment](https://www.wearedevelopers.com/videos/1152-ai-model-management-life-circles-ml-ops-for-generative-ai-models-from-research-to-deployment)
- [Cyber Sleuth: Finding Hidden Connections in Cyber Data](https://www.wearedevelopers.com/videos/893-cyber-sleuth-finding-hidden-connections-in-cyber-data)

## Related Articles

- [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production)
- [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer)
- [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers)
- [Top Big Data Technologies That You Need to Know](https://www.wearedevelopers.com/magazine/108-top-big-data-technologies-that-you-need-to-know)
- [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift)
- [Data Engineer Salary UK](https://www.wearedevelopers.com/magazine/253-data-engineer-salary-uk)