Data Engineer, AI Enablement

AbbVies 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 AbbVies 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 AbbVies 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. 

Qualifications :

Required:

• Bachelors Degree with 5 years of experience; OR Masters 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. 

Additional Information :

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 Companys sole and absolute discretion unless and until paid and may be modified at the Companys 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. 

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Work :

No

Employment Type :

Full-time