Data Engineer(Neo4j)

Key Responsibilities

- Model Complex Banking Data in Neo4j: Design and implement graph data models representing customers, accounts, transactions, devices, and their interconnected relationships.

- Apply Graph Data Science (GDS) Algorithms: Leverage Community Detection, Link Prediction, Node Embeddings, and Pathfinding algorithms to uncover hidden fraud patterns and suspicious networks. - Build Real-Time Investigation Dashboards: Develop interactive visualisations using Neo4j Bloom to empower Risk and AML teams with actionable insights.

- Collaborate Across Teams: Partner closely with Risk Management, Anti-Money Laundering (AML), Compliance, and Data Science teams to translate business requirements into technical solutions that reduce fraud losses.

- Optimise Performance: Ensure scalability, performance tuning, and reliability of graph databases in production environments.

- Drive Innovation: Stay current with emerging graph technologies and fraud detection techniques, and contribute to continuous improvement of our analytics capabilities.

Must-Have

- 5–6 years of overall IT experience, with 2+ years of hands-on experience working with Neo4j, Cypher query language, and Graph Data Science (GDS) library.

- Strong proficiency in Python for ETL pipelines, data processing, and integration with Neo4j GDS workflows.

- Solid understanding of graph database concepts, including data modelling, indexing, query optimisation, and performance tuning.

- Experience applying GDS algorithms such as Community Detection (Louvain, Label Propagation), Link Prediction, Node Embeddings (Node2Vec, GraphSAGE), and Centrality measures.

- Familiarity with Neo4j Bloom or similar graph visualisation tools for building investigative dashboards.

- Experience in the Banking, Fraud Detection, or AML domain is highly preferred.

- Strong analytical and problem-solving skills with the ability to translate complex business requirements into technical solutions.

- Excellent communication and collaboration skills to work effectively with cross-functional teams.

Good-to-Have

- Experience with other graph databases (e.g., Amazon Neptune, TigerGraph, JanusGraph).

- Knowledge of machine learning frameworks (e.g., scikit-learn, TensorFlow, PyTorch) and integrating ML models with graph analytics.

- Familiarity with cloud platforms (AWS, Azure, GCP) and deploying Neo4j in cloud environments.

- Understanding of data streaming technologies (Kafka, Kinesis) for real-time fraud detection pipelines.

- Experience with CI/CD pipelines, Infrastructure as Code (Terraform, CloudFormation), and DevOps practices.

- Knowledge of regulatory frameworks related to AML, KYC, and financial crime compliance.

- Neo4j Certified Professional or Graph Data Science certification is a plus.