Role Overview:

We are looking for a Lead Graph Data Engineer to design and develop scalable Graph Database and Knowledge Graph solutions for banking use cases. The role will involve building graph-based data models, semantic layers and AI-enabled solutions while working closely with Data Engineering, Architecture, AI/ML, Risk, Compliance and Business teams.

Key Responsibilities:

- Design, develop and optimize Graph Database solutions using Neo4j.

- Develop complex and performance-optimized Cypher queries for graph traversal, analysis and data retrieval.

- Design graph data models, including nodes, relationships, properties, indexes and constraints.

- Build and maintain Knowledge Graphs to connect structured and unstructured banking data.

- Develop Ontologies, taxonomies and semantic models to represent business entities and relationships.

- Apply Semantic Web concepts, including RDF, OWL and SPARQL, where applicable.

- Develop data pipelines for ingestion, transformation, cleansing and integration of data into graph platforms.

- Write complex SQL queries for data extraction, transformation, validation and reconciliation.

- Integrate graph databases with enterprise data platforms, APIs, data lakes and other data sources.

- Leverage NLP, LLMs and RAG to develop graph-enabled GenAI and intelligent data solutions.

- Design Graph-enhanced RAG solutions combining Knowledge Graphs, embeddings and vector search.

- Support banking use cases including Customer 360, fraud detection, AML/KYC, risk analytics, transaction analysis, counterparty mapping and relationship intelligence.

- Ensure data quality, lineage, governance, security and scalability of graph solutions.

- Work with architects and technology teams to define enterprise graph and semantic data architecture.

- Lead technical design, PoCs and implementation of graph-based solutions.

- Mentor engineers and establish best practices for graph modelling, query optimization and data engineering.

Mandatory Skills:

- Strong hands-on experience in Neo4j and Cypher

- Expertise in Graph Databases and Graph Data Modelling

- Strong understanding of Knowledge Graphs

- Experience in Ontology / Ontology Modelling

- Strong SQL and data engineering skills

- Understanding of Semantic Technologies / Semantic Web

- Exposure to NLP, LLM and RAG

- Experience integrating multiple structured and unstructured data sources

- Strong understanding of data quality, governance and security