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:
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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 communication, with the ability to frame tradeoffs, risks, dependencies, and progress in a clear and practical way for 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, or linked-data concepts.
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 to analytics, 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.
Applicable only to applicants applying to a position in any location with pay disclosure requirements under state or local law:
The compensation range described below is the range of possible base pay compensation that the Company believes in good faith it will pay for this role at the time of this posting based on the job grade for this position. Individual compensation paid within this range will depend on many factors including geographic location, and we may ultimately pay more or less than the posted range. This range may be modified in the future.
We offer a comprehensive package of benefits including paid time off (vacation, holidays, sick), medical/dental/vision insurance and 401(k) to eligible employees.
This job is eligible to participate in our short-term incentive programs.
Note: No amount of pay is considered to be wages or compensation until such amount is earned, vested, and determinable. The amount and availability of any bonus, commission, incentive, benefits, or any other form of compensation and benefits that are allocable to a particular employee remains in the Company's sole and absolute discretion unless and until paid and may be modified at the Company’s sole and absolute discretion, consistent with applicable law.
AbbVie is an equal opportunity employer and is committed to operating with integrity, driving innovation, transforming lives and serving our community. Equal Opportunity Employer/Veterans/Disabled.
US & Puerto Rico only - to learn more, visit https://www.abbvie.com/join-us/equal-employment-opportunity-employer.html
US & Puerto Rico applicants seeking a reasonable accommodation, click here to learn more:
AbbVie is a global biopharmaceutical company specializing in immunology, oncology, neuroscience, and eye care. The company develops and markets treatments including Humira, Skyrizi, and Rinvoq.
