
Data Modeler
- Hyderabad, Telangana
- Permanent
- Full-time
- Minimum of 10 years' experience of Data management and modelling solutions working as a Data Modeller within the Financial Services sector is essential; preferably in a Treasury/Finance function and or related front office environment.
- A proven track record working in a large and global banking environment is desirable.
- Demonstrate experience in design data modelling solutions (conceptual, logical and application/messaging) with corresponding phasing, transitions, and migrations where necessary.
- Good understanding of managing 'data as a product (asset)' principle across enterprise domains and technology landscapes.
- Good understanding of architectural domains (business, data, application, and technology)
- Good communication skills with the ability to influence and present data models (as well as concepts) to technology and business stakeholders.
- Good collaboration skills with the ability to demonstrate experience achieving outcomes in a matrixed environment partnering with data modellers from other domains to build and join shared and reusable data assets.
- Experience of working with Agile and Scrum in a large scalable Agile environment. This should include participation and progress reporting in daily standups.
- Data standards, data governance, data strategy and data lineage would be advantageous in this role.
- Knowledge of reference/master data management
- Cloud exposure to solutions implemented in either GCP, AWS or Azure would be beneficial as well as having exposure to big data solutions would be advantageous.
- Experience working with leading data modelling tools modelling documentation using tools such as Visual Paradigm, ERwin, PowerDesigner, ER Studio etc.
- Knowledge of data modelling standards and modelling technical documentation using Entity Relationship Diagrams (ERD) or Unified Modelling language (UML) or BIAN.
- Results oriented with ability to produce solutions that deliver organisational benefit.
- Understanding of issue and data quality management, prioritisation, business case development, remediation planning and tactical or strategic solution delivery
- Exposure with data governance initiatives such as lineage, masking, retention policy, and data quality
- Strong analytical skills and problem-solving, with the ability to work unsupervised and take ownership for key deliverables.
- Exposure with ETL architectures and tools, including data virtualisation, integration with APIs is desirable.