We are seeking a highly skilled Enterprise Data Modeler to design, develop, and maintain enterprise-level data models that support our banking operations, analytics, and regulatory compliance. The ideal candidate will have a deep understanding of financial services data structures, a strong grasp of data governance principles, and experience working in complex, regulated environments.

REQUIRED QUALIFICATIONS:

• Bachelor’s or master’s degree in computer science, Information Systems, Data Science, or a related field.
• 5+ years of experience in data modelling, ideally within banking or financial services.
• Expertise in data modelling tools such as ERwin, IBM Infosphere Data Architect, or similar.
• Strong understanding of relational, dimensional, and data vault modelling techniques.
• Experience working with data warehouse architectures, data lakes, and cloud-based platforms (Azure).
• Solid knowledge of banking systems and data domains (e.g., customer, account, transaction, product, risk, compliance).
• Familiarity with data governance frameworks and regulatory compliance requirements (e.g., BCBS 239, GDPR).
• Strong SQL skills and understanding of database platforms (Oracle, SQL Server, Postgress, DB2).
• Datawarehouse design methodologies understanding.

PREFERRED QUALIFICATIONS:

• Experience with master data management (MDM) and metadata management tools.
• Knowledge of real-time data modelling for payments and fraud detection.
• Exposure to big data technologies (e.g., Hadoop, Spark) and streaming platforms (Confluent).

• Design and maintain conceptual, logical, and physical data models across banking domains (e.g., Retail, Corporate, Risk, Compliance).
• Collaborate with business and IT teams to gather requirements and translate them into scalable and compliant data models.
• Ensure data models support regulatory requirements (e.g., BCBS 239, AML, KYC, Basel III) and internal data governance policies.
• Work closely with data architects, engineers, and analysts to implement models in data warehouses, data lakes, and operational systems.
• Drive standardization of data definitions and data quality metrics across the enterprise.
• Support data lineage, metadata management, and data cataloguing initiatives to enhance data transparency and traceability.
• Develop and maintain documentation of models, data dictionaries, and data flows.
• Assist in M&A data integration and system consolidation projects.
• Stay current with industry trends, data modelling tools, and regulatory changes affecting data structures.