Graph DB Engineer

EXL; Gurugram District; On-site; INR 1,200,000 - 2,400,000; Full time

Job summary

EXL is seeking a seasoned Graph Data Engineer to build and operate the enterprise entity graph. You will implement node and edge projections, loading pipelines, and multi-hop traversal capabilities that capture ownership and affiliation relationships back to source data.

You will expert in graph databases (Neo4j, Fabric Graph, Cosmos DB, TigerGraph, Neptune) and query languages (GQL, Cypher, Gremlin), and you will partner with analytics and data engineering teams to deliver scalable graph

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.

Responsibilities

• Graph schema implementation and edge/node design per architect.
• Relational-to-graph projection with full source attribution.
• Develop and optimize graph load processes and CDC-driven re-projections.
• Model relationships including ownership and corporate structures.
• Write and optimize multi-hop graph queries for complex traversals.

Skills

Graph data modelling

PySpark

SQL engineering

Relational-to-graph projection

Data engineering

Tools

Fabric Graph

Neo4j

Cosmos DB (Gremlin)

TigerGraph

Neptune

GQL

Cypher

Gremlin

OneLake

Spark notebooks

Job description

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.

Required Skills & Experience

Skill Area

Specific Requirements

Graph Databases

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

Graph Query

GQL (ISO/IEC 39075), Cypher or Gremlin; multi-hop traversal, path queries, pattern matching, query optimisation

Microsoft Fabric

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

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

Quality & Ops

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)
• Implemented graph schema and node/edge projection logic
• Baseline entity graph populated in Fabric
• Incremental re-projection on CDC
• Graph quality validation checks and performance tuning results

Dual Role / Complementary Skills

Highly 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.