AI Enablement Data Engineer: Trusted Data Pipelines

BioSpace

North Chicago (IL)

On-site

USD 120,000 - 180,000

Full time

4 days ago

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Benefits offered by this job

Medical, dental, vision insurance

401(k) plan

Paid time off

Job summary

AbbVie’s BTS Information Research (IR) team seeks a Data Engineer, AI Enablement to design, build, and operate scalable data pipelines and curated data products that make R&D data AI-ready and easy to discover. You will contribute to ARCH knowledge graphs, data governance, and reliable data foundations that support analytics, knowledge graphs, and machine learning across research programs.

You will collaborate with data scientists, ML engineers, platform teams, and data owners to translate

Qualifications

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, ML, 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 teams to translate requirements and deliver fit‑for‑purpose data assets.
Ability to operate with autonomy, manage priorities across concurrent workstreams, and communicate clearly.
Demonstrated ability to learn and apply new data engineering and AI-enablement technologies and to serve as a technical resource for others.
Experience providing input and reviewing outputs from contracted or vendor engineers without direct reporting authority.
Strong communication, planning, and organizational skills for technical, scientific, and business audiences.
Data product engineering mindset with emphasis on reusable, scalable data assets.
Data curation and stewardship mindset focusing on quality, metadata, lineage, governance, and documentation.
Technical fluency across data platforms, pipelines, orchestration, cloud environments, and data delivery practices.
Operational discipline across monitoring, troubleshooting, and continuous improvement.
Coordination and influence to guide work and coordinate across contributors.

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.
Trusted Data Foundation Enablement: Establish reliable, scalable data foundations that support analytics, reporting, knowledge graph capabilities, ML, and AI-enabled use cases.
AI, RAG & Knowledge Graph Readiness: Prepare data and documents for AI and knowledge discovery use cases, including labeling and embedding workflows.
Data Quality, Governance & Documentation: Apply data quality and governance practices to ensure trust and compliance across data assets.
Technical Coordination & Delivery Support: Translate requirements into usable AI-ready data products, review outputs, and support delivery against quality standards.
Operational Reliability & Continuous Improvement: Monitor pipelines, data freshness, cost, failures, and delivery issues, and improve patterns and automation.
Compliance & Standards: Follow corporate policies including data security, lifecycle practices, and governance standards.