remotely.living

Senior Software Engineer

emagine · Remote - Poland · 2026-09-11

Apply for this job

Job description

- Work mode - fully remote.

- Assignment type: B2B contract.

- Start - ASAP/1 month.

- Contract length > 10-12 months + extensions.

- Language - English.

- Industry - pharmaceutical.

- Recruitment process - 2 interviews with the client.

- Workload: Full time.

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 the 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 (Cypher, schema design, 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 (UniProt, Ensembl, ChEMBL, etc.).

- 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.