Fraud Data Modeler

JD Key Responsibilities: Data Modeling & Architecture: o Design and implement conceptual, logical, and physical data models to support fraud data management across systems such as Hunter, Falcon, and other fraud detection systems. o Develop and manage entity mappings to ensure data integrity across the fraud data pipeline, specifically for staging, persistent, and consumption layers in the Fraud Data Hub. o Apply SCD2 (Slowly Changing Dimensions) techniques for effective data versioning and history tracking across different layers. o Utilize modeling best practices for structured and unstructured data sets from various sources, ensuring scalability and performance in the cloud environment. Data Ingestion: o Ensure accurate data ingestion and transformation from various sources (files, databases, APIs) into the GCP environment. o Work with the team to streamline ETL (Extract, Transform, Load) processes, ensuring seamless movement of fraud data between different layers. Collaboration with Stakeholders: o Work closely with business users, fraud risk managers, and other stakeholders to understand their data requirements and ensure that the data modeling solutions meet business and regulatory needs. o Engage with data engineers to implement and optimize data pipelines for high-volume and high-velocity fraud data. Data Governance & Quality: o Ensure that the data models align with ANZs data governance policies and maintain data quality, accuracy, and consistency. o Collaborate with data quality teams to identify and resolve issues related to data integrity in fraud risk data systems. Technology & Best Practices: o Utilize GCP-based technologies for managing, processing, and modeling fraud data, ensuring scalability and performance of fraud risk systems. o Implement and follow best practices for data modeling, ensuring that designs are both efficient and sustainable. Key Skills & Experience: Experience & Knowledge: o Requires understanding of Fraud Prevention & Fraud data management. o Good understanding of Banking, Financial Crime Management is necessary o Proven experience in data modeling for fraud risk management, preferably within a large financial institution (banking experience is highly desirable). o Strong knowledge of fraud detection systems like Hunter, Falcon, or similar fraud management platforms. o Experience with data modeling for layered architectures (staging, persistent, and consumption layers) and knowledge of SCD2 (Slowly Changing Dimensions) logic. o Hands-on experience with GCP (Google Cloud Platform) or similar cloud platforms, specifically for data warehousing, processing, and modeling. Technical Skills: o Proficiency in SQL, data modeling tools, and ETL technologies. o Familiarity with data warehousing concepts and technologies (BigQuery, DataProc, etc.). o Experience with file-based data ingestion, transformation, and processing. o Understanding of data governance, data security, and regulatory requirements, especially within fraud and risk environments. o Data Modeling Tools & Technologies (for logical, conceptual, and physical data modeling): Erwin Data Modeler, IBM InfoSphere Data Architect, Oracle SQL Developer Data Modeler, or similar modeling tools. Lucidchart or Microsoft Visio for visual representation of data models and workflows. Cloud-native tools such as GCP BigQuery, Cloud SQL, DataFlow, and DataPrep for modeling and transformation. Experience in metadata management and the use of Data Catalogs (e.g., GCP Data Catalog, Alation, or Collibra) for data governance and lineage tracking. Analytical & Communication Skills: o Excellent analytical skills with the ability to translate business requirements into data modeling solutions. o Strong communication skills to interact effectively with both technical teams (data engineers, architects) and business stakeholders (fraud risk managers, business analysts).