Database Engineer — Knowledge Graph Platform

Mindbox is a tech-driven company connecting top IT talents with technology projects for leading enterprises across Europe.

• 3+ years in data/database engineering, including graph platform implementation.
• Experience in major graph ecosystems:
  • LPG stack: Cypher/Gremlin; platforms like Neo4j, FalkorDB, JanusGraph.
  • RDF stack: SPARQL; platforms like GraphDB, Stardog, Blazegraph, Neptune RDF.
• Proficiency in graph standards (RDF, RDFS, OWL, SHACL) and ontology governance/versioning best practices.
• Hands-on delivery experience on Google Cloud Platform (GCP) including GKE, Cloud Run, Pub/Sub, Dataflow, BigQuery, IAM, dashboards/monitoring.
• Practical LLM integration exposure (embeddings, vector/hybrid retrieval, GraphRAG workflows).
• Strong foundations in Python and/or Java/Scala/Node.js, API integration, CI/CD pipelines, and Infrastructure-as-Code (Terraform, GitOps).
• Proven track record in leading engineering efforts and delivering iterative platform roadmaps.
• Clear communication across technical and non-technical stakeholders, ability to translate business objectives into platform capabilities.
• Commitment to engineering excellence, proactive incident management, and continuous improvement culture.

Nice to have:

• Multi-tenant architecture and platform productization experience.
• Knowledge of responsible AI practices, model guardrails, and evaluation frameworks.
• Experience with Kubernetes operators and advanced observability stacks (Prometheus, Grafana, OpenTelemetry).
• Familiarity with FinOps strategies for high-memory graph workloads.

• Own the end-to-end graph platform lifecycle: architecture, deployment, reliability, and continuous evolution.
• Design graph schemas and ontologies across LPG and RDF paradigms, ensuring standards-based governance and version control.
• Build and operate scalable data ingestion pipelines (batch and streaming) with data quality and lineage frameworks.
• Implement robust observability and reliability practices aligned with SRE principles on Google Cloud Platform (monitoring, alerting, HA).
• Define engineering guardrails for performance tuning, query optimization, and cost-efficient scaling.
• Collaborate with product, data, and security stakeholders to deliver reusable platform services for internal teams.
• Activate AI-driven reasoning and retrieval scenarios leveraging LLMs, embeddings, vector databases, and hybrid GraphRAG workflows.