Fraud Data Modeler _C2

Key Responsibilities:

Data Modeling & Architecture: oDesign and implement conceptual logical and physical data models to support fraud data management across systems such as Hunter Falcon and other fraud detection systems.

oDevelop 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.

oApply SCD2 (Slowly Changing Dimensions) techniques for effective data versioning and history tracking across different layers. oUtilize modeling best practices for structured and unstructured data sets from various sources ensuring scalability and performance in the cloud environment.

Data Ingestion: oEnsure accurate data ingestion and transformation from various sources (files databases APIs) into the GCP environment.

oWork with the team to streamline ETL (Extract Transform Load) processes ensuring seamless movement of fraud data between different layers.

Collaboration with Stakeholders: oWork 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. oEngage with data engineers to implement and optimize data pipelines for high-volume and high-velocity fraud data.

Data Governance & Quality:

oEnsure that the data models align with ANZs data governance policies and maintain data quality accuracy and consistency.

oCollaborate with data quality teams to identify and resolve issues related to data integrity in fraud risk data systems.

Technology & Best Practices: oUtilize GCP-based technologies for managing processing and modeling fraud data ensuring scalability and performance of fraud risk systems. oImplement and follow best practices for data modeling ensuring that designs are both efficient and sustainable.

Key Skills & Experience: Experience & Knowledge: oRequires understanding of Fraud Prevention & Fraud data management.

oGood understanding of Banking Financial Crime Management is necessary

oProven experience in data modeling for fraud risk management preferably within a large financial institution (banking experience is highly desirable).

oStrong knowledge of fraud detection systems like Hunter Falcon or similar fraud management platforms.

oExperience with data modeling for layered architectures (staging persistent and consumption layers) and knowledge of SCD2 (Slowly Changing Dimensions) logic. oHands-on experience with GCP (Google Cloud Platform) or similar cloud platforms specifically for data warehousing processing and modeling.

Technical Skills: oProficiency in SQL data modeling tools and ETL technologies. oFamiliarity with data warehousing concepts and technologies (BigQuery DataProc etc.). oExperience with file-based data ingestion transformation and processing.

oUnderstanding of data governance data security and regulatory requirements especially within fraud and risk environments. oData 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: oExcellent analytical skills with the ability to translate business requirements into data modeling solutions. oStrong communication skills to interact effectively with both technical teams (data engineers architects) and business stakeholders (fraud risk managers business analysts).