data engineer for AI systems

Enfint; Barcelona; Híbrido; EUR 65.000 - 90.000; Jornada completa

Descripción de la vacante

Enfint is seeking an experienced Data/AI Engineer to design graph schemas, build semantic indexing systems, and orchestrate AWS-based data infrastructure for enterprise-scale AI systems. You will ingest diverse data sources, create ontologies, and implement robust ETL/ELT pipelines while ensuring data quality and governance.

The role requires strong Python/SQL skills, expertise with query languages (Gremlin, SPARQL, Cypher), and hands-on experience with AWS services like S3, Glue, Neptune, and

Formación

• 4–5 years of experience in Data Engineering, Software Engineering, or AI Engineering with production deployments.
• Advanced Python and SQL proficiency.
• Practical experience modeling and developing queries with Gremlin, SPARQL, and/or Cypher.
• Solid AWS experience with S3, Glue, and Aurora PostgreSQL.
• Experience designing batch and incremental ETL/ELT pipelines.
• Experience with Docker, CI/CD, and Git.
• Experience with hybrid retrieval architectures combining SQL, graph, and vector search.
• Knowledge of data lineage, cataloging, metadata management, and data quality frameworks.
• Degree or Master's in Computer Science, Mathematics, Physics, Engineering, or a related field.
• Spanish at C1 (Expert) level and English at B2 (Advanced) level.
• Nice to have: Neptune and AWS Bedrock.

Responsabilidades

• Design graph schemas, entity resolution strategies, ontological structures, and semantic indexing systems to support agentic AI.
• Ingest and transform structured and unstructured data from S3, SharePoint, FileNet, and other sources with semantic consistency.
• Create taxonomies and ontologies using RDF, OWL, and SKOS standards.
• Operate and orchestrate AWS services including S3, Glue, Aurora PostgreSQL, Neptune, and AWS Bedrock.
• Build RAG pipelines, semantic search, metadata enrichment, and Text-to-SQL interfaces.
• Design graph retrieval strategies combining pattern matching, vector search, full-text search, and relational traversals.
• Ensure quality control, monitoring, and production readiness of AI data infrastructure.

Descripción del empleo

act digital designs semantic data layers and knowledge base solutions that power enterprise-scale artificial intelligence systems.

Задачи

• Design graph schemas, entity resolution strategies, ontological structures, and semantic indexing systems to support agentic AI
• Ingest and transform structured and unstructured data from S3, SharePoint, FileNet, and other sources while ensuring semantic consistency
• Create taxonomies and ontologies using RDF, OWL, and SKOS standards
• Operate and orchestrate AWS services including S3, Glue, Aurora PostgreSQL, Neptune, and AWS Bedrock
• Build RAG pipelines, semantic search, metadata enrichment, and Text-to-SQL interfaces
• Design graph retrieval strategies combining pattern matching, vector search, full-text search, and relational traversals
• Ensure quality control, monitoring, and production readiness of AI data infrastructure

Требования

• 4–5 Years of experience in Data Engineering, Software Engineering, or AI Engineering with solutions deployed in production
• Advanced Python (P3 - Advanced) and SQL proficiency
• Practical experience modeling and developing queries with Gremlin, SPARQL, and/or Cypher
• Solid AWS experience with S3, Glue, and Aurora PostgreSQL
• Experience designing batch and incremental ETL/ELT pipelines
• Experience with Docker, CI/CD, and Git
• Experience with hybrid retrieval architectures combining SQL, graph, and vector search
• Knowledge of data lineage, cataloging, metadata management, and data quality frameworks
• Degree or Master's in Computer Science, Mathematics, Physics, Engineering, or a related field
• Spanish at C1 (Expert) level
• English at B2 (Advanced) level
• Nice to have: Neptune and AWS Bedrock

Условия

Hybrid schedule with 40% telework / remote work; Office schedule.