Neo4j Data Engineer

Join a team to build innovative Neo4j-powered solutions that detect fraud rings, money laundering networks, and account takeovers in real-time. You'll work at the intersection of data science, graph analytics, and financial crime prevention—helping our clients in the banking sector safeguard their operations and protect their customers.

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 usingNeo4j Bloom to empower Risk and AML teams with actionable insights.

- CollaborateAcross Teams: Partner closely with Risk Management, Anti-Money Laundering(AML), Compliance, and Data Science teams to translate business requirementsinto 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.

Requirements

Must-Have

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

- Strongproficiency in Python for ETL pipelines, data processing, and integration withNeo4j GDS workflows.

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

- Experienceapplying GDS algorithms such as Community Detection (Louvain, LabelPropagation), Link Prediction, Node Embeddings (Node2Vec, GraphSAGE), andCentrality measures.

- Familiaritywith Neo4j Bloom or similar graph visualisation tools for buildinginvestigative dashboards.

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

- Stronganalytical and problem-solving skills with the ability to translate complexbusiness requirements into technical solutions.

- Excellentcommunication and collaboration skills to work effectively withcross-functional teams.

Good-to-Have

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

- Knowledgeof machine learning frameworks (e.g., scikit-learn, TensorFlow, PyTorch) andintegrating ML models with graph analytics.

- Familiaritywith cloud platforms (AWS, Azure, GCP) and deploying Neo4j in cloudenvironments.

- Understandingof data streaming technologies (Kafka, Kinesis) for real-time fraud detectionpipelines.

- Experiencewith CI/CD pipelines, Infrastructure as Code (Terraform, CloudFormation), andDevOps practices.

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

- Neo4jCertified Professional or Graph Data Science certification is a plus.

- Bachelor'sor Master's degree in Computer Science, Data Science, Information Technology,or a related field.

- 5-6years of experience in IT production, preferably in banking or financialservices

- Goodproblem-solving skills and ability to work under pressure in a fast-pacedenvironment.

- Strongcommunication skills with the ability to liaise effectively across teams.

Interested candidates may send their CV to MAC (Reg No. R1221300) ***email_hidden*** quoting the job title in the Subject line. We regret that only shortlisted candidates will be notified.