EXL
Website:
exlservice.com
Job details:
Role Purpose
Build the Entity Graph. This role converts the resolved, canonical entity data into a production graph implementing node and edge projections, loading and optimising the graph, enabling multi-hop traversal, and validating that the graph faithfully represents entity, ownership and affiliation relationships back to source.
Key Responsibilities
- Graph schema implementation implement the graph schema defined by the architect: node types, edge types, properties, keys and constraints.
- Relational-to-graph projection build and operate the projection logic that converts canonical relational entity tables into graph nodes and edges, with full source attribution.
- Graph loading & pipelines — develop and optimise graph load processes; implement incremental re-projection triggered by change data capture.
- Relationship modelling — implement entity-to-entity, ownership (parent/subsidiary), officer/director and affiliation relationships, including bitemporal handling where required.
- Traversal & query engineering — write and optimise graph queries (GQL/Cypher-style) supporting multi-hop traversal scenarios such as corporate family trees and shared-agent affiliations.
- Performance optimisation — tune graph queries and load performance; profile traversal cost; address scale bottlenecks.
- Graph quality validation — implement automated checks on node/edge counts, orphan detection, relationship integrity and source traceability.
- Consumption support — support exposure of graph data through API and SQL endpoints, and collaborate on GraphRAG indexing over graph projections.
Must-Have Qualifications
- 7+ years data engineering with 3+ years hands-on graph database development
- Proven experience modelling and loading a production graph from relational sources
- Strong graph query language proficiency (GQL, Cypher or Gremlin)
- Solid PySpark and SQL engineering skills
- Experience with hierarchical/ownership data structures and recursive relationships
Nice-to-Have
- Microsoft Fabric Graph experience (native LPG on OneLake)
- Exposure to GraphRAG or graph-based retrieval
- Experience in corporate entity, KYC, fraud-network or supply-chain graph domains
- Graph algorithms (community detection, centrality, shortest path)
Key Deliverables Owned
- Implemented graph schema and node/edge projection logic
- Baseline entity graph populated in Fabric
- Incremental re-projection on CDC
- Validated multi-hop traversal scenarios
- Graph quality validation checks and performance tuning results
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