Role Purpose

nBuild 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.

nKey Responsibilities

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- Graph schema implementation — implement the graph schema defined by the architect: node types, edge types, properties, keys and constraints.
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- 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.
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- Graph loading & pipelines — develop and optimise graph load processes; implement incremental re-projection triggered by change data capture.
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- Relationship modelling — implement entity-to-entity, ownership (parent/subsidiary), officer/director and affiliation relationships, including bitemporal handling where required.
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- 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.
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- Performance optimisation — tune graph queries and load performance; profile traversal cost; address scale bottlenecks.
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- Graph quality validation — implement automated checks on node/edge counts, orphan detection, relationship integrity and source traceability.
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- Consumption support — support exposure of graph data through API and SQL endpoints, and collaborate on GraphRAG indexing over graph projections.
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nRequired Skills & Experience

nSkill Area

nSpecific Requirements

nGraph Databases

nHands-on with one or more of: Fabric Graph, Neo4j, Cosmos DB (Gremlin), TigerGraph, Neptune. Strong LPG modelling

nGraph Query

nGQL (ISO/IEC 39075),



Cypher or Gremlin; multi-hop traversal, path queries, pattern matching, query optimisation

nData Engineering

nPython/PySpark, SQL, Delta Lake, ETL/ELT pipeline development, incremental/CDC processing

nMicrosoft Fabric

nLakehouse, OneLake, Spark notebooks, Data Factory pipelines, SQL analytics endpoint

nModelling

nConverting relational schemas to graph models, key/edge design, handling many-to-many and hierarchical structures

nQuality & Ops

nGraph validation techniques, monitoring, troubleshooting load failures, documentation

nMust-Have Qualifications

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- 7+ years data engineering with 3+ years hands-on graph database development
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- Proven experience modelling and loading a production graph from relational sources
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- Solid graph query language proficiency (GQL, Cypher or Gremlin)
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- Solid PySpark and SQL engineering skills
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- Experience with hierarchical/ownership data structures and recursive relationships
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nNice-to-Have

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- Microsoft Fabric Graph experience (native LPG on OneLake)
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- Exposure to GraphRAG or graph-based retrieval
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- Experience in corporate entity, KYC, fraud-network or supply-chain graph domains
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- Graph algorithms (community detection, centrality, shortest path)
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nKey Deliverables Owned

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- Implemented graph schema and node/edge projection logic
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- Baseline entity graph populated in Fabric
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- Incremental re-projection on CDC
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- Validated multi-hop traversal scenarios
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- Graph quality validation checks and performance tuning results
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nDual Role / Complementary Skills

nHighly complementary with the VectorDB Engineer (Role 4) — GraphRAG requires graph traversal and vector retrieval working together. If consolidating headcount, these two roles can be merged into a single "Graph & Vector Engineer", since the vector workload is concentrated in Phase 2 while graph schema work runs earlier. Also cross-covers with the Data Engineer on Spark-based pipeline work.

📌 Graph DB Engineer (Gurugram)
🏢 EXL
📍 Gurugram