Knowledge Graph & Data Infrastructure Engineer

LHH; Valenzano; In loco; EUR 45.000 - 65.000; Tempo pieno

Descrizione del lavoro

Un'azienda internazionale di consulenza cerca un Knowledge Graph & Data Infrastructure Engineer che si occupi della progettazione e implementazione di un Knowledge Graph. La figura avrà il compito di gestire architetture distribuite in ambienti cloud e costruire pipeline ETL. È necessaria un'esperienza di 3–6+ anni con database a grafo come Neo4j e competenze in Python. Sono offerti opportunità di crescita in un ambiente dinamico e inclusivo.

Competenze

• 3–6+ anni di esperienza come Data Engineer o Knowledge Graph Engineer.
• Esperienza pratica con database a grafo e ambienti cloud.
• Competenze nella progettazione di modelli di dati complessi.

Mansioni

• Progettare e implementare strategie e architetture per un Knowledge Graph.
• Ottimizzare le query per l'accesso ai dati.
• Costruire pipeline ETL per trasformare documenti in entità grafiche.

Conoscenze

Esperienza con database a grafo

Competenze in Python, Java o Scala

Progettazione e implementazione di architetture distribuite

Costruzione di API per accesso ai dati

Esperienza con ETL/ELT pipeline

Strumenti

Neo4j

AWS

GraphQL

Descrizione del lavoro

Knowledge Graph & Data Infrastructure Engineer

For our client, an international consulting company expert in building SAAS platforms, we are seeking a Knowledge Graph & Data Infrastructure Engineer.

Responsibilities

• Design and implement the strategy, architecture, and underlying schema for a Knowledge Graph.
• 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 a hybrid retrieval API that unifies graph and vector results for 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.

Qualifications

• 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.»