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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Graph Engineer (Neo4j, Python, Cloud) - **Company:** Cognizant Technology Solutions Corporation - **Location:** Chicago, IL, United States (Remote available) - **Experience:** Experienced - **Salary:** $88,000.0 - $135,000.0 - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Amazon Web Services, Microsoft Azure, Bioinformatics, Cloud Computing, Databases, Continuous Integration, Information Engineering, Data Governance, Extract Transform Load (ETL), Database Queries, Graph Database, Python (Programming Language), Laboratory Information Management Systems, Neo4j, Performance Tuning, Unstructured Data, Cloud Platform System, Flask (Web Framework), Knowledge Engineering, Git, Fastapi, Pandas, Information Technology, Api Design, Software Version Control, Data Pipelines, GXP - **Published:** October 1, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=4abb4feae415dce7 ## About the Role * 3-4 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 GCP) storage, compute, and managed database services * Understanding 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 ## Description 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., * 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. We strive to provide flexibility wherever possible. Based on this role's business requirements, this is a remote position open to qualified applicants in the United States. Regardless of your working arrangement, we are here to support a healthy work-life balance though our various wellbeing programs. The working arrangements for this role are accurate as of the date of posting. This may change based on the project you're engaged in, as well as business and client requirements. Rest assured; we will always be clear about role expectations. ## Related Videos - [Cyber Sleuth: Finding Hidden Connections in Cyber Data](https://www.wearedevelopers.com/videos/893-cyber-sleuth-finding-hidden-connections-in-cyber-data) - [Putting the Graph In GraphQL With The Neo4j GraphQL Library](https://www.wearedevelopers.com/videos/257-putting-the-graph-in-graphql-with-the-neo4j-graphql-library) - [Blueprints for Success: Steering a Global Data & AI Architecture](https://www.wearedevelopers.com/videos/1577-blueprints-for-success-steering-a-global-data-ai-architecture) - [How a Small Team Shrank a Microsoft Monorepo by 94%](https://www.wearedevelopers.com/videos/1236-how-a-small-team-shrank-a-microsoft-monorepo-by-94) - [Graphs and RAGs Everywhere... 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