You will drive the critical bridge between complex data engineering and banking compliance, transforming unstructured policy documentation into an automated, high-integrity knowledge graph system. By defining semantic schemas, validating logical data nodes, and overseeing query optimization, you will directly safeguard regulatory accuracy across the enterprise.
Responsibilities
- Collaborate with business stakeholders and subject matter experts to define and refine ontology schemas representing banking policies.
- Validate the quality of automated data extraction from unstructured policy documents to ensure accurate entity relationships.
- Conduct manual and automated testing of graph structures to identify inconsistencies or logical fallacies.
- Act as the primary technical interface between data science/engineering teams and business stakeholders in Compliance and Risk.
- Document data lineage, business rules, and mapping requirements for ingestion pipelines.
- Design and execute comprehensive test plans for SPARQL or Cypher-based queries to optimize performance and accuracy.
- At least 3 years as a Technical Business Analyst, Data Analyst, or similar role within financial services.
- Proven understanding of knowledge graph concepts including ontologies, RDF/OWL, or property graphs.
- Proficiency in query languages such as SPARQL, Cypher, and SQL.
- Ability to interpret technical schemas, data models, and LLM extraction-based outputs.
- Familiarity with graph management platforms like Protege, GraphDB, Neo4j, or similar tools.
- Exceptional communication skills to simplify complex technical concepts for non-technical business stakeholders.