We are seeking an experienced Knowledge & Semantic Data Engineer to join our Data, Analytics & AI group for Operations IT.
This group supports various business areas such as Develop, Manufacture, Supply Chain, Quality, and Procurement with data-driven solutions and insights.
Typical Accountabilities: Strategy: Provide strategy & vision for knowledge graph and semantic engineering: - Partner with the AI team to develop a strategy for a Gen AI chatbot to query Operations data products via a knowledge graph.
- Guide the development, execution, and implementation of a roadmap for the strategies above.
- Accountable for being responsible for change management activities.
Knowledge Graph Platform: - Leverage your expertise in graph databases and data engineering to design, develop, and maintain advanced knowledge-based systems and solutions that support our digital initiatives.
- Collaborate with cross-functional teams and stakeholders to understand business requirements, identify knowledge gaps, and define data-driven strategies to inform decision-making and drive business growth.
- Develop and implement advanced data engineering pipelines, including data ingestion, processing, storage, and analysis, ensuring the optimal performance, scalability, and reliability of our data infrastructure.
- Utilize your expertise in Graph DB and Graph query languages to continue developing and maintain existing data models and graph databases, enabling efficient querying and analysis of complex data relationships.
- Actively contribute to the continuous improvement of our knowledge engineering processes, tools, and methodologies, fostering a culture of innovation and excellence within the team.
- Stay abreast of emerging technologies and industry trends to continuously enhance the organization's knowledge and semantic capabilities.
Semantic Engineering: - Maintain, evolve, and grow our knowledge models (ontologies), communicating, selling them, and annotating them to provide common understanding.
- Collaborate with multi-functional teams to define and document data models using ontological principles.
- Conduct ontology mapping and integration to facilitate data interoperability and integration across systems.
- Provide training and guidance to collaborators on ontology usage and standard processes for data organization.
- Stay updated on industry trends and advancements in ontology development to enhance data management processes.
Stakeholder Collaboration: - Collaborate with collaborators across departments to understand requirements and ensure business needs are met.
- Act as a liaison between IT and Business Units, fostering strong communication and understanding.
- Oversight of partnerships with multiple third parties, academic or outsourced partners to shape and drive project outcomes and knowledge building.
- Deliver business education, improving awareness of KG capabilities, potential use-cases, and key benefits within the wider business.
Quality Assurance: - Implement and be responsible for robust quality assurance processes to ensure the accuracy, reliability, and security of data.
- Establish and monitor key performance indicators to measure the effectiveness of Knowledge Graph services.
YOU WILL NEED TO HAVE: Knowledge Graph and Semantic Analysis: Multi-disciplinary capabilities to explain, design, and create powerful knowledge graph models.
Balancing business and technical understanding is essential.
Knowledge Representation and Semantic Technologies: Sound understanding of OWL, RDF, SWRL, SPARQL, JSON-LD, semantic modelling, data integration, data unification, knowledge graph design, ontology, and taxonomy.
Use Case Awareness: Awareness of the breadth of use cases for these technologies.
Prudent user interface design that exploits the power of graphs while mitigating visualization challenges.
Experience with Graph / Triple Stores: Familiarity with technologies like PoolParty, Stardog, TigerGraph, Ontotext GraphDB, Grakn, MarkLogic, Metaphactory, and Neo4j.
Data and Information Architecture: Understand data and information architecture, including modeling languages such as UML, ER, IDEF, and Data Flow Diagrams.
Excellent Communication Skills: Be an effective, passionate, trusted advocate and communicator for Graph and Semantic Technologies.
Thinking Skills: Excellent problem-solving, analytical, and critical thinking skills, with a keen attention to detail.
Focused on Gen AI: Designing, developing, and deploying customized and scalable knowledge graph solutions or products with emphasis on graph data modelling, graph traversal, graph theory, and graph analytics or graph AI.
Experience with developing LLM models on top of knowledge graph is essential.
Graph Query Languages: Deep knowledge in Cypher, Gremlin, or any other major GQL.
Other Databases: Working knowledge of a range of databases.
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