Senior GraphDB Engineer

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