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) atanc1@anchorsearchgroup.com quoting the job title in the Subject line. We regret that only shortlisted candidates will be notified.