Database Engineer - Graph Platform
⚲ Kraków
28 000–33 000 zł netto (+ VAT) / mies.
Wymagania
- Cypher
- Gremlin
- Neo4j
- FalkorDB
- JanusGraph
- SPARQL
- GraphDB
- Stardog
- Blazegraph
- Neptune RDF
- Google Cloud Platform
- Terraform
- GitOps
Opis stanowiska
Nasze wymagania:
3+ years in data/database engineering, including graph platform implementation.
Experienced in both 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.
Mile widziane:
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.
O projekcie:
We are seeking a Database Engineer to design, build, and scale Knowledge Graph capabilities across the enterprise environment, driving integrated graph solutions and AI-enabled use cases. This role combines deep graph database expertise, cloud platform engineering on GCP, and LLM integration for advanced retrieval patterns such as RAG and GraphRAG.
If you have strong technical skills across LPG and RDF ecosystems, production-grade delivery experience, and passion for graph-powered AI workflows, this position is for you.
Sounds like your kind of challenge?
Zakres obowiązków:
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 GCP (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.
Note: Detailed project information will be shared during the recruitment process.
Oferujemy:
Flexible cooperation model
Hybrid work setup – 8 times per month from the office
Collaborative team culture – work alongside experienced professionals eager to share knowledge
Continuous development – access to training platforms and growth opportunities
Comprehensive benefits – including Interpolska Health Care, Multisport card, Warta Insurance, and more
High quality equipment – laptop and essential software provided
3+ years in data/database engineering, including graph platform implementation.
Experienced in both 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.
Mile widziane:
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.
O projekcie:
We are seeking a Database Engineer to design, build, and scale Knowledge Graph capabilities across the enterprise environment, driving integrated graph solutions and AI-enabled use cases. This role combines deep graph database expertise, cloud platform engineering on GCP, and LLM integration for advanced retrieval patterns such as RAG and GraphRAG.
If you have strong technical skills across LPG and RDF ecosystems, production-grade delivery experience, and passion for graph-powered AI workflows, this position is for you.
Sounds like your kind of challenge?
Zakres obowiązków:
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 GCP (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.
Note: Detailed project information will be shared during the recruitment process.
Oferujemy:
Flexible cooperation model
Hybrid work setup – 8 times per month from the office
Collaborative team culture – work alongside experienced professionals eager to share knowledge
Continuous development – access to training platforms and growth opportunities
Comprehensive benefits – including Interpolska Health Care, Multisport card, Warta Insurance, and more
High quality equipment – laptop and essential software provided
🔍 Dekoder Ogłoszenia
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Experienced in both major graph ecosystems
Wymóg biegłości w dwóch odrębnych stosach grafowych — wysokie oczekiwania, potencjalnie trudne do spełnienia przez jednego kandydata.
🟡
Practical LLM integration exposure
Modne hasło; 'exposure' bywa mgliste i może oznaczać powierzchowne zadania lub eksperymenty bez jasno określonego celu.
🟡
Proven track record in leading
Oczekiwanie przywództwa mimo braku szczegółów — może oznaczać nieformalne obowiązki managerskie bez wsparcia.