Data Engineer Neo4J

Avensys is a reputed global IT professional services company headquartered in Singapore. Our service spectrum includes enterprise solution consulting, business intelligence, business process automation and managed services. Given our decade of success, we have evolved to become one of the top trusted providers in Singapore and service a client base across banking and financial services, insurance, information technology, healthcare, retail and supply chain.

Job Description

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.

Preferred Qualifications and Experience

- 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.

Qualifications:

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

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

- Good problem-solving skills and ability to work under pressure in a fast-paced environment.

- Strong communication skills with the ability to liaise effectively across teams.

WHAT'S ON OFFER

You will be remunerated with an excellent base salary and entitled to attractive company benefits. Additionally, you will get the opportunity to enjoy a fun and collaborative work environment, alongside a strong career progression.

To submit your application, please apply online or submit your CV to [Confidential Information], Your interest will be treated with strict confidentiality.

CONSULTANT DETAILS

Consultant Name : Ashwak Ahmed

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