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. Qualifications:
• 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 protected] quoting the job title in the Subject line. We regret that only shortlisted candidates will be notified.