Eisen: - Experience of working with Graphwise PoolParty software. - Experience designing, developing, and managing taxonomies and ontologies for enterprise use. - Strong understanding of metadata modeling, governance, and versioning to ensure consistency across systems. - Familiarity with auto-tagging implementation and optimization using taxonomy management systems. - Experience configuring and maintaining taxonomies for automated tagging in SharePoint. - Knowledge of integrating taxonomy structures with API-driven workflows, including MuleSoft-based implementations. - Understanding of SKOS, RDF, OWL, SPARQL, and other semantic web standards for structuring taxonomies and ontologies. - Ability to conduct taxonomy audits, gap analyses, and performance monitoring to improve tagging accuracy. - Experience collaborating with engineering, IT, and business teams to align taxonomy efforts with enterprise data strategy. - Strong analytical and problem-solving skills for refining taxonomy structures and ensuring usability. Wensen: - Ability to document taxonomy frameworks, tagging guidelines, and governance policies for long-term scalability. Omschrijving: The Knowledge Management (KM) Capability is crucial for enhancing the organization’s ability to manage and utilize knowledge effectively. This role involves developing a comprehensive technical architecture for taxonomy and ontology management systems. The engineer will select the appropriate technology stack that aligns with the organization’s infrastructure. Engaging with stakeholders is essential, as the engineer will provide training and support on the new systems. The role includes designing proof of concepts (PoCs) to validate the technical framework and oversee their execution. Security is a priority; thus, the engineer will work with the security team to ensure compliance with relevant standards. Access management is another critical aspect, where the engineer will define and configure SSO requirements. Integration with internal systems is necessary to ensure seamless data flow and functionality. Quality assurance and testing protocols will be established to ensure system reliability. Documentation is vital for knowledge transfer and ongoing support. The engineer will also monitor system performance post-implementation and provide technical support as needed. Identifying risks and contributing to governance frameworks will help maintain data integrity and quality. Overall, this role is pivotal in driving the organization’s knowledge management strategy forward.