Consultor de Datos Semánticos y Grafos

• 2-4 years of experience in data roles, with hands-on exposure to graph databases or ontology engineering.

• Working knowledge of at least one graph database platform: Neo4j, Amazon Neptune, Azure Cosmos DB, StarDog or similar.

• Practical experience with SPARQL and/or Gremlin / Cypher.

• Understanding of ontology fundamentals: RDF, RDFS, OWL - able to read and write ontology definitions.

• Solid foundation in data engineering: ETL/ELT pipelines, data modelling, cloud storage (AWS S3 / Azure ADLS).

• Familiarity with at least one cloud platform (AWS or Azure) at a working level.

• Analytical mindset: able to map business concepts to formal graph structures.

• Good written and verbal communication in English; additional languages valued.

• Academic background in Computer Science, Information Science, Linguistics, or related fields with exposure to knowledge representation or semantic web.

• Exposure to GenAI or LLM integration concepts: understanding of how graph structures support retrieval-augmented generation or semantic search.

• Familiarity with data governance or metadata management platforms.

• Certification or coursework in graph technologies (Neo4j, cloud platforms) valued.

• Experience in any industry vertical with complex domain models: financial services, energy, healthcare, retail.

At CONVOTIS Iberia we are looking for a hands-on practitioner in graph data modelling and semantic technologies, working embedded in delivery teams under the guidance of a Senior Data Architect. Contributes to the design, implementation, and testing of knowledge graph solutions and ontology-based data integrations across industries and cloud platforms. A profile that combines technical execution capability with growing client-facing skills.

• Implement graph data models under architectural guidance: property graphs, RDF/OWL ontologies, entity-relationship schemas.

• Develop and optimize graph queries in SPARQL, Gremlin, and/or Cypher for data retrieval, traversal, and analytics use cases.

• Build and maintain data ingestion pipelines feeding graph databases from structured and semi-structured enterprise sources.

• Apply ontology validation rules using SHACL and support ontology versioning and maintenance processes.

• Integrate graph layers with data catalogues, metadata platforms, and downstream analytics or AI consumers.

• Participate in testing and validation of graph models against business requirements: entity coverage, relationship accuracy, inference correctness.

• Support pre-sales activities: contribute to technical sections of proposals, prepare PoC environments, document reference architectures.

• Proactively develop expertise across graph platforms and semantic standards; share knowledge within the practice.