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.
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.
Key job details
About Virtusa
Teamwork, quality of life, professional and personal development: values that Virtusa is proud to embody. When you join us, you join a team of 36,000 people globally that cares about your growth — one that seeks to provide you with exciting projects, opportunities and work with state of the art technologies throughout your career with us.
Great minds, great potential: it all comes together at Virtusa. We value collaboration and the team environment of our company, and seek to provide great minds with a dynamic place to nurture new ideas and foster excellence.
Virtusa is an Equal Opportunity Employer. All applicants will receive fair and impartial treatment without regard to race, color, religion, sex, national origin, ancestry, age, legally protected physical or mental disability, protected veteran status, status in the U.S. uniformed services, sexual orientation, gender identity or expression, marital status, genetic information or on any other basis which is protected under applicable federal, state or local law.
Applicants may be required to attend interviews in person or by video conference. In addition, candidates may be required to present their current state or government-issued ID during each interview. All candidates must be authorized to work in the USA.
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