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.