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 ANZ's 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).