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Data Graph Engineer - Chicago, IL - Onsite

LEO DOES IT INCChicago, IL🇺🇸United StatesPosted Oct 22, 2026

Quick Overview

Seniority
Mid Senior
Work mode
Hybrid
Location
Chicago, IL, United States
Posted
23 hours ago

Job Description

Hi,
Hope you're doing well !
Please find the go through the below Job description and let me know if you are comfortable with the below position.
Job Description: Graph Engineer (Neo4j, Python, Cloud) - 3 profiles
Minimum Experience: 5+ years
Contract : W2
Domain: Pharma R&D - Discovery & Clinical Documentation
Location: Chicago, IL
About the Role
We're looking for a Graph Engineer to join our Data & Knowledge Engineering team supporting Pharma R&D initiatives spanning drug discovery and clinical documentation. You'll design, build, and maintain graph-based data models that connect scientific, experimental, and regulatory data - enabling researchers and scientists to uncover relationships across compounds, targets, trials, and documents that traditional relational systems struggle to surface.
Required Skills & Qualifications
  • 5+ years of hands-on experience with Neo4j (or comparable graph database) - schema design, Cypher query writing, and performance tuning
  • Strong Python skills for data pipelines, ETL, and API development (pandas, py2neo/neo4j-driver, FastAPI/Flask a plus)
  • Working experience with at least one major cloud platform (AWS, Azure, or Google Cloud Platform) - storage, compute, and managed database services
  • Understanding of data modeling concepts (relational-to-graph translation, ontology/taxonomy basics)
  • Familiarity with version control (Git) and CI/CD basics
  • Bachelor's degree in Computer Science, Bioinformatics, Data Engineering, or related field
Nice to have:
  • Exposure to life sciences data standards (FHIR, CDISC, UMLS, MeSH)
  • Experience with NLP/entity extraction for unstructured documents
  • Knowledge of RDF/SPARQL or other semantic web technologies
  • Roles & Responsibilities
  • Design and implement graph data models that represent entities and relationships across drug discovery pipelines (targets, compounds, assays, pathways) and clinical documentation (trials, protocols, adverse events, regulatory submissions).
  • Build and maintain ETL/ELT pipelines in Python to ingest structured and unstructured data from source systems (LIMS, ELN, clinical trial databases, document repositories) into Neo4j.
  • Develop and optimize Cypher queries and APIs that power search, recommendation, and knowledge-discovery features used by scientists and clinical documentation teams.
  • Support entity resolution and linkage across disparate data sources - connecting compounds, genes, diseases, and clinical documents into a unified knowledge graph.
  • Deploy and manage graph infrastructure on cloud platforms, including provisioning Neo4j instances (AuraDB or self-managed), monitoring performance, and managing access controls in compliance with data governance policies.
  • Collaborate with cross-functional stakeholders - data scientists, clinical documentation specialists, and R&D researchers - to translate domain requirements into scalable graph schemas and queries.
  • Ensure data quality, lineage, and compliance with pharma industry standards (GxP, HIPAA where applicable) throughout the graph data lifecycle, including documentation of data models and pipeline logic for audit readiness.
Thanks
Sai Bharat

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