Knowledge Engineer
Role Overview
The financial services organization is investing in data and AI capabilities and seeks an experienced Knowledge Engineer to build and scale its enterprise knowledge graph.
The graph will connect fragmented business data into a trusted semantic foundation for analytics, regulatory and risk use cases, and AI applications.
The role owns the enterprise knowledge graph and ontology layer, supporting data science, risk, compliance, operations, and AI/GenAI teams.
The position is based on site in Abu Dhabi, Monday through Friday, on an initial 12-month contract with potential extension.
Key Skills for This Role
Full Job Posting
Opportunity
The financial services organization is investing in data and AI capabilities and seeks an experienced Knowledge Engineer to build and scale its enterprise knowledge graph.
The graph will connect fragmented business data into a trusted semantic foundation for analytics, regulatory and risk use cases, and AI applications.
The role owns the enterprise knowledge graph and ontology layer, supporting data science, risk, compliance, operations, and AI/GenAI teams.
The position is based on site in Abu Dhabi, Monday through Friday, on an initial 12-month contract with potential extension.
Key Responsibilities
- Design and build enterprise-scale knowledge graphs from conceptual model through production deployment.
- Develop and maintain ontologies, taxonomies, and semantic data models for financial services domains.
- Capture business domain knowledge and translate it into formal, reusable models.
- Integrate multiple source systems with data engineering teams, addressing ingestion, entity resolution, and data quality.
- Define standards for metadata, lineage, governance, and ontology change management.
- Build and optimize graph queries and APIs for customer views, network analysis, financial crime, AML/KYC, risk, and regulatory reporting.
- Support GraphRAG, semantic search, and LLM grounding initiatives.
- Align the graph with industry standards and reference models such as FIBO where appropriate.
- Contribute to architecture roadmaps, tooling selection, and knowledge management practices.
- Document designs, develop training materials, and upskill internal teams.
Essential Experience
- Financial services or banking background and knowledge of the data, business, and regulatory landscape.
- Production delivery of enterprise-grade knowledge graphs, beyond proofs of concept.
- Hands-on ontology and data modeling experience with RDF/OWL and/or labeled property graphs.
- Experience with an enterprise graph platform such as Neo4j, Stardog, TigerGraph, Amazon Neptune, or GraphDB.
- Proficiency in SPARQL and/or Cypher/Gremlin.
- Experience integrating complex heterogeneous data, entity resolution, and master/reference data.
- Understanding of data governance, metadata management, and data quality in regulated environments.
- Strong stakeholder management and communication of semantic concepts to non-technical audiences.
Desirable Experience
- Familiarity with FIBO or other financial industry ontologies and standards.
- Experience with SHACL/ShEx, semantic validation, or ontology lifecycle management.
- Exposure to GraphRAG, LLM integration, vector search, or NLP-based knowledge extraction.
- Python or similar programming, data pipelines, or Azure, AWS, or GCP platforms.
- Experience supporting AML, KYC, fraud, risk, or regulatory reporting with graph technology.
- GCC/Middle East financial services experience and a relevant degree are desirable.
Key Competencies
Structured, analytical thinking and clear modeling of complex domains.
Pragmatic, delivery-focused approach balancing semantic rigor with business value.
Strong communication and collaboration across technical and business teams.
Comfort operating in a large, regulated, fast-moving organization.
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