Virtusa
Data Modelling
Job Description
Data Modelling, Design and develop data models for CASA, Loan, and Credit Services, ensuring they meet business requirements and compliance standards.
Create conceptual, logical, and physical data models that support the bank's strategic objectives.
Ensure data models are optimized for performance, security, and scalability to support business operations and analytics.
Maintain and manage metadata for data models, ensuring version control and proper documentation.
Validate and revise data models as per evolving business needs and regulatory requirements.
Collaboration with Data Architect:
Work closely with the Data Architect to establish the overall data architecture strategy and framework.
Contribute to the definition of data model structures within a data mesh environment.
Cross Department Coordination
Collaborate with stakeholders from various departments, including Business Intelligence, Risk Management, Compliance, and Operations, to gather requirements and ensure alignment.
Facilitate workshops and discussions to ensure all parties understand data modeling processes and outputs.
Data Quality and Governance
Ensure data quality and integrity in the data models by implementing best practices in data governance.
Assist in the establishment of data management policies and standards.
Conduct regular data audits and reviews to ensure data accuracy and consistency across systems.
Documentation and Communication
Prepare comprehensive documentation for data models, including data dictionaries, flow diagrams, and metadata.
Communicate effectively with technical and non-technical stakeholders to explain complex data concepts.
Key Technologies & Tools:
Data Modelling Tools ERwin, IBM InfoSphere Data Architect, Oracle Data Modeler, Microsoft Visio, or similar tools.
Databases: SQL, Oracle, MySQL, MS SQL Server, PostgreSQL, Neo4j Graph
Data Warehousing Technologies: Snowflake, Teradata, or similar.
ETL Tools Informatica, Talend, Apache NiFi, Microsoft SSIS, or similar.
Big Data Technologies Hadoop, Spark optional but preferred.
Cloud Technologies Experience with data modelling on cloud platforms Microsoft Azure Synapse, Data Factory
Programming Languages SQL, Python, or R for data analysis and validation is a plus
Knowledge of API and microservices architectures is a plus.
Knowledge of machine learning or advanced analytics models in banking is a plus.
Certifications in data modelling is a plus.
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