The role
From AbbVie's own posting.
AbbVie’s Business Technology Solutions (BTS) Information Research (IR) organization is seeking a Data Engineer, AI Enablement to help 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 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, and operate scalable 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 by providing technical guidance to contracted engineers supporting the same data products, translating requirements into clear work, reviewing outputs, helping remove barriers, and ensuring results meet agreed quality, documentation, and acceptance standards. 
Under the direction of the Associate Director – Data Strategy, AI & Knowledge Enablement, this role is an opportunity to contribute at the center of AbbVie’s R&D data transformation. The data foundations you build will help determine which analytics, knowledge graph, and AI use cases are possible across research — and how confidently the organization can use their output to support scientific decision-making. 
Responsibilities 
AI-Ready Data Product Engineering:  Design, build, and operate curated, 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:  Establish reliable, 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, supporting chunking, 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. Provide technical guidance to contracted engineers, clarify work, review outputs, help remove barriers, and support delivery against agreed quality and acceptance standards. 
Operational Reliability & Continuous Improvement:  Monitor pipeline performance, data freshness, cost, failures, and delivery issues; troubleshoot and resolve problems before they impact data 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, including GxP compliance, data security, software development lifecycle practices, data governance standards, and relevant regulatory or contractual requirements. 
Required:
Bachelor’s Degree with 5 years of experience; OR Master’s Degree with 4 years of experience in information technology, data engineering, data management, analytics, life sciences, or a related field.
Hands-on experience designing, developing, and operating production data pipelines and curated data products using SQL, Python, ETL/ELT patterns, and workflow orchestration tools such as Airflow. 
Working knowledge of modern data platforms, data integration, data warehousing or lakehouse patterns, distributed SQL or big data environments, cloud infrastructure, and analytics enablement. 
Experience preparing data for downstream analytics, machine learning, knowledge graph, 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 requirements and deliver fit-for-purpose data assets. 
Ability to operate with a high degree of autonomy, manage priorities across concurrent workstreams, modify approach when needed, escalate open issues, and keep stakeholders informed through clear written and verbal communication. 
Demonstrated ability to learn, understand, and apply new data engineering, platform, and AI-enablement technologies, and to serve as a technical resource for others. 
Experience providing technical input, clarifying requirements, and reviewing outputs from contracted, vendor, or managed-services engineers without direct reporting authority. 
Strong communication, planning, and organizational skills, with the ability to explain technical concepts and keep stakeholders informed. 
Data product engineering mindset, with the ability to shape reusable, well-structured data assets that are practical, scalable, and fit for analytics and AI-enabled use. 
Data curation and stewardship mindset, with attention to quality, metadata, lineage, governance, standards, documentation, and appropriate use. 
Technical fluency across data platforms, pipelines, integration patterns, orchestration, cloud environments, and data delivery practices sufficient to work effectively with engineering and platform teams. 
Operational discipline across monitoring, troubleshooting, prioritization, issue resolution, automation, reusable patterns, and continuous improvement. 
Technical coordination and influence, with the ability to clarify priorities, guide work, review outputs, resolve ambiguity, and coordinate across internal and external contributors. 
Stakeholder commu