Infrastructure Data Engineer
Descrizione dell'offerta
For our client, international consulting company expert in SAAS platforms building, we are looking for a:
Knowledge Graph & Data Infrastructure Engineer
Set up a federated graph architecture that separates public knowledge from private client data.
Build the hybrid retrieval API that unifies graph and vector results for the reasoning agents.
Implement data normalization, entity resolution, and incremental updates to keep the graph clean and scalable.
· Ensure data quality and temporal consistency across the knowledge base.
· Optimize database performance through indexing, caching, and query tuning.
· Implement access control, encryption, audit logging, and compliance mechanisms across the data layer.
3–6+ years of professional experience as a Data Engineer, Backend Engineer, or Knowledge Graph Engineer, working on production-grade data systems.
~ Strong hands-on experience with graph databases (e.g. Solid experience designing and operating distributed data infrastructure in cloud environments (AWS, GCP, or Azure).
~ Proven ability to design data models and schemas for complex domains, including entities, relationships, and metadata.
~ Experience implementing ETL / ELT pipelines for structured and unstructured data, including incremental updates and backfills.
~ Strong understanding of data consistency, versioning, and provenance in large-scale data systems.
~ graph DB + relational DB + document store + object storage) into a coherent data platform.
~ Practical experience building APIs for data access (REST and/or GraphQL) and integrating them into backend services.
~ Strong skills in Python, Java, or Scala for backend and data engineering workloads.
~ Experience with query optimization, indexing strategies, caching, and partitioning for high-performance data access.
~ Understanding of security, access control, and data isolation in multi-tenant SaaS platforms.
~ Strong grasp of software engineering best practices: version control, testing, CI/CD, monitoring, and documentation.
«L'azienda promuove un ambiente di lavoro inclusivo e garantisce pari opportunità a tutte le persone, indipendentemente da genere, età, origine, orientamento sessuale o disabilità.»
Knowledge Graph & Data Infrastructure Engineer
Set up a federated graph architecture that separates public knowledge from private client data.
Build the hybrid retrieval API that unifies graph and vector results for the reasoning agents.
Implement data normalization, entity resolution, and incremental updates to keep the graph clean and scalable.
· Ensure data quality and temporal consistency across the knowledge base.
· Optimize database performance through indexing, caching, and query tuning.
· Implement access control, encryption, audit logging, and compliance mechanisms across the data layer.
3–6+ years of professional experience as a Data Engineer, Backend Engineer, or Knowledge Graph Engineer, working on production-grade data systems.
~ Strong hands-on experience with graph databases (e.g. Solid experience designing and operating distributed data infrastructure in cloud environments (AWS, GCP, or Azure).
~ Proven ability to design data models and schemas for complex domains, including entities, relationships, and metadata.
~ Experience implementing ETL / ELT pipelines for structured and unstructured data, including incremental updates and backfills.
~ Strong understanding of data consistency, versioning, and provenance in large-scale data systems.
~ graph DB + relational DB + document store + object storage) into a coherent data platform.
~ Practical experience building APIs for data access (REST and/or GraphQL) and integrating them into backend services.
~ Strong skills in Python, Java, or Scala for backend and data engineering workloads.
~ Experience with query optimization, indexing strategies, caching, and partitioning for high-performance data access.
~ Understanding of security, access control, and data isolation in multi-tenant SaaS platforms.
~ Strong grasp of software engineering best practices: version control, testing, CI/CD, monitoring, and documentation.
«L'azienda promuove un ambiente di lavoro inclusivo e garantisce pari opportunità a tutte le persone, indipendentemente da genere, età, origine, orientamento sessuale o disabilità.»