Senior Graph Data Engineer (Cloud) (m/f/d)

We are seeking a talented and motivated Senior Graph Data Engineer (Cloud) (f/m/d) to help us design, deploy, and operate a production-grade graph database service.

ESQlabs is an innovative, internationally acting Contract Research Organization and a global leader in the development and application of the OSP Suite (www.open-systems-pharmacology.org). We are a research-focused provider of specialized computational analyses in the life sciences industry.

Role

As part of the MPSlabs team and in close collaboration with multidisciplinary collaborators, you will help us establish a robust graph database service for execution within a project. The tasks associated with this role are (but not limited to):

• Schema design: A maintainable property-graph schema with clear node/relationship types, properties, constraints, and indexes.
• Cloud deployment and integration: Infrastructure-as-Code procedures and CI/CD to provision and initialize the graph DB with the agreed schema; secure connectivity to relevant data sources so the database is ready to be populated.
• Performance and operations: Database configuration, tuning, observability (metrics, logs, traces), usage monitoring, SLOs/alerts, backup/restore, and cost-aware scale-up/scale-out strategies.
• Schema exploration: Reproducible introspection via built-in tools and notebooks to visualize/list labels, relationship types, property keys, constraints, and indexes.
• Query development: Optimized queries for neighborhood discovery, shortest paths, recurring motifs/shapes, and structural introspection.
• Graph algorithms and pattern detection: Workflows for triangles/stars/chains, community detection, centrality, and link prediction; encode domain patterns provided by collaborators.
• Statistical analysis and graph ML: Descriptive stats (counts, degree and path length distributions, clustering coefficient, density), centrality reports, community summaries, embeddings, and simple ML pipelines (clustering/classification) using graph-derived features.
• ML tools integration: Interfaces and containers to integrate analysis tools from other sources.
• Iterative analysis and reporting: Versioned analytics and reports that update with data changes; documented assumptions and changelogs.

Required skills & experience

• Graph databases and query languages
• Production experience with at least one major platform: Neo4j (Cypher, APOC, GDS), AWS Neptune (Gremlin/SPARQL), Azure Cosmos DB for Gremlin, TigerGraph, or ArangoDB.
• Strong schema/constraint/index design; query profiling and optimization; practical understanding of cardinality and selectivity.

• Graph algorithms and analytics
• Hands-on with shortest path, motif searches, centrality (PageRank, betweenness, closeness), community detection (e.g., Louvain/Leiden), and basic link prediction.
• Ability to compute and interpret graph statistics (node/edge counts, degree and path length distributions, clustering coefficient, density).

• Data engineering and integration
  • Building secure ingestion/ELT pipelines from APIs, object storage, and databases with validation, schema evolution, and idempotent loads.

• Cloud, security, and DevOps
  • Deploying managed graph services or self-managed clusters on AWS/Azure/GCP.
  • Infrastructure as Code (Terraform or CloudFormation/Bicep), containers (Docker), CI/CD (GitHub Actions/GitLab CI/Azure DevOps).
  • Observability (CloudWatch/Prometheus/Grafana), centralized logging, alerting, backup/restore, and disaster recovery.
  • Security best practices: IAM/roles, VPC design, secrets management, TLS in transit/at rest, least-privilege access, and auditability.

• Communication and documentation
  • Clear technical writing (runbooks, ADRs, user guides); stakeholder communication; ability to translate domain patterns into graph designs and queries.

Further qualities that will put you in the spotlight

• Graph ML and embeddings: node2vec/DeepWalk/ GraphSAGE; familiarity with PyTorch Geometric or DGL; evaluation and basic MLOps hygiene.
• Visualization: Neo4j Bloom, yFiles, Graphistry, Cytoscape; lightweight app dashboards (Streamlit/Plotly Dash).
• Orchestration and data movement: Airflow/Prefect, event streaming (Kafka), and batch scheduling.
• Biomedical data familiarity: integrating outputs from biomedical data pipelines; awareness of FAIR data, metadata standards, and co