Data&Knowledge platform Engineer

Role Overview We are seeking an experienced Senior Data & Knowledge Platform Engineer to design, build and operate the data foundations behind SICPA’s sovereign intelligence and AI initiatives. The role combines production data engineering with entity-centric, graph and semantic data modelling. You will turn heterogeneous internal, government and open-source data into trusted, traceable and reusable data products that support analytics, knowledge graphs, AI agents and decision-support applications. Key responsibilities Design and implement scalable pipelines for structured, unstructured, geospatial, OSINT ( Open source intelligence) and SICPA data. Define reusable data models supporting relational, graph, geospatial and AI workloads. Build capabilities for data quality, enrichment, entity resolution, provenance and lineage. Develop and operate data platforms, repositories and APIs for applications such as SICPA Pulse. Operationalise taxonomies, ontologies and knowledge graphs. Prepare reliable data pipelines for RAG systems and AI agents. Implement testing, CI/CD, monitoring and observability for data products. Collaborate with data scientists, software engineers, domain experts and academic partners to productionise analytical and graph-based solutions. Required qualifications Master’s degree in Computer Science, Data Engineering, Software Engineering or a related field, with 5+ years of experience building production data platforms. Strong proficiency in Python and SQL. Experience integrating heterogeneous data from APIs, databases, files or messaging systems. Solid knowledge of data modelling, schema evolution, data quality, lineage and provenance. Experience with workflow orchestration, automated testing, Docker and CI/CD. Experience with relational databases and at least one additional data technology such as graph, document, vector, search or geospatial databases. Strong software-engineering and problem-solving skills. Excellent communication skills with technical and non-technical stakeholders Preferred qualifications Knowledge graphs and technologies such as Neo4j, Cypher, RDF or SPARQL. Ontology, taxonomy or semantic-data modelling. Entity resolution, record linkage and temporal data modelling. Geospatial data engineering, including PostGIS or GeoJSON. Data pipelines for RAG, LLM and agentic systems. Experience with OSINT, multilingual, government, financial or fraud-related data. Familiarity with graph analytics or graph machine learning. Experience with Azure, AWS, hybrid or on-premise deployments.