Own data quality: Define and enforce data validation, provenance tracking, and quality metrics across all data sources in the graph
Integrate data sources: Work closely with internal and external data providers to ingest, normalize, and harmonize heterogeneous biomedical datasets
Maintain the knowledge graph: Own the Neo4j schema, data modeling, and pipeline reliability
Prepare data for AI/analytics: Ensure graph data is structured and trustworthy enough to support downstream AI and analytics use cases – including the Blindspot Analysis
Collaborate cross-functionally: Work with research scientists, data scientists, and engineers to translate scientific needs into reliable data solutions
Our requirements
Strong Python skills for data engineering and pipeline development
Hands-on experience with Neo4j (Cypher, schema design, query optimization)
Proven experience integrating multiple heterogeneous biomedical data sources
Deep understanding of data quality practices in production environments
Experience working directly with data providers to resolve schema, identifier, and consistency issues
Familiarity with biomedical standards and identifiers (UniProt, Ensembl, ChEMBL, etc.)
Strong communication skills with cross-functional scientific and technical teams
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