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Knowledge Graph & Data Infrastructure Engineer
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Key Responsibilities
Design and implement the Strategy Knowledge Graph and its underlying schema.
Build mechanisms to track how entities and relationships change over time.
Set up a federated graph architecture that separates public knowledge from private client data.
Define node and edge structures with essential metadata and provenance.
Optimize graph queries and retrieval patterns for complex reasoning tasks.
Manage and integrate multiple storage systems (graph, vector, metadata, documents, blobs).
Maintain consistency across storage layers through event-driven synchronization.
Build the hybrid retrieval API that unifies graph and vector results for the reasoning agents.
Implement caching and partitioning strategies to ensure performance and multi-tenant scalability.
Build ETL pipelines to ingest processed documents and transform them into graph entities and relationships.
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.
Maintain full provenance so every fact is traceable to its source.
Develop APIs (REST/GraphQL) for querying, updating, and interacting with the Knowledge Graph.
Optimize database performance through indexing, caching, and query tuning.
Implement access control, encryption, audit logging, and compliance mechanisms across the data layer.
Profilo Experience & Background
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. Neo4j, TigerGraph, Amazon Neptune, or equivalent) including schema design, query optimization, and performance tuning.
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.
Experience integrating multiple storage systems (e.g., 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.
Familiarity with event-driven architectures (e.g., message queues, streaming, async pipelines) for synchronizing distributed systems.
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.
«I candidati, nel rispetto del D.lgs. 198/2006, D.lgs. 215/2003 e D.lgs. 216/2003, sono invitati a leggere l’informativa sulla privacy consultabile sotto il form di richiesta dati della pagina di candidatura (Regolamento UE n. 2016/679). L’offerta è rivolta a candidati di entrambi i sessi, nel rispetto del D.lgs. 198/2006 e della normativa vigente in materia di pari opportunità. 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.»
Seniority level: Mid‑Senior level
Employment type: Full-time
Job function: Information Technology (Human Resources Services)
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