Data Engineer - Junior Level - Part-Time

Junior Data Engineer

Mandatory skill - Neo4j

Job Description
Role Summary
Join Our 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.
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 visualization's 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.
• Optimize 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, contribute to continuous improvement of our analytics capabilities.

Qualifications
Must-Have

• 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 optimization, and performance tuning.
• Experience applying GDS algorithms such as Community Detection (Louvain, Label Propagation), Link Prediction, Node Embeddings (Node2Vec, Graph SAGE), and Centrality measures.
• Familiarity with Neo4j Bloom or similar graph visualization 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, Tiger Graph, Janus Graph).

• 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 deployingNeo4j 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.

• 2-3 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.

Ideal profile:
Data governance specialist x1
• People with experience in managing data quality frameworks and data issue resolution processes, who understand banking business & tech and can drive discussion + progress with biz, tech and data stakeholders to deep dive into data quality issues, align on root causes, data ownership, solutioning and implementation of fixes.

• People that can define processes and create guides, playbooks, training materials in data governance areas such as data quality, data ownership, metadata management. Someone who has experience in data governance, is able to quickly grasp the bank's data governance policies, and has excellent capabilities to translate that into intuitive, efficient processes that can be implemented at scale, supported by user-friendly playbooks.
AI Governance specialist x1

• People with hands-on experience in AI governance that can contribute to expand the bank AI governance frameworks and work with Platform Owners/Tech for the implementation of underlying capabilities, e.g. definition of AI agent policies, risk controls across the AI agent's lifecycle.

• Same as above on process guides, playbooks and training materials, in the AI Governance / Responsible AI area.