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September 3, 2026

Senior Knowledge Graph Engineer – Python / Neo4j

Senior • Remote

Warsaw, Poland

Work mode: Fully remote

Assignment type: B2B

Start: 01.09.2026

Contract length: More than 10–12 months, with extensions

Language: English

Industry: Pharmaceutical

Recruitment process: 2 interviews with the client

Workload: Full time

Description

This role is for a Senior Software Engineer – Knowledge Graph, responsible for maintaining data quality, integration, and development of the knowledge graph platform, a vital asset that consolidates diverse drug discovery and gene biology data.

Responsibilities

  • Own data quality: Define and enforce data validation, provenance tracking, and quality metrics across all data sources in the graph.
  • Integrate data sources: Collaborate with internal and external data providers to ingest, normalize, and harmonize heterogeneous biomedical datasets.
  • Maintain the knowledge graph: Manage the Neo4j schema, data modeling, and pipeline reliability.
  • Prepare data for AI/analytics: Ensure graph data supports AI and analytics use cases, including Blindspot Analysis.
  • Collaborate cross-functionally: Work with research scientists, data scientists, and engineers to align scientific needs with reliable data solutions.

Must Haves

  • Strong Python skills for data engineering and pipeline development.
  • Hands-on experience with Neo4j, including Cypher, schema design, and query optimization.
  • Proven experience integrating multiple heterogeneous biomedical data sources.
  • Deep understanding of data quality practices in production environments.
  • Experience resolving schema, identifier, and consistency issues directly with data providers.
  • Familiarity with biomedical standards and identifiers, including UniProt, Ensembl, and ChEMBL.
  • Strong communication skills with cross-functional scientific and technical teams.

Nice to Haves

  • Experience in pharma, biotech, or academic drug discovery.
  • Advanced degree (MSc/PhD) in a relevant field.
  • Familiarity with graph ML, embeddings, or search/retrieval concepts.

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