Data Modeller

1. Role - Data Modeller
2. Required Technical Skill Set Conceptual, Logical & Physical Data Modeling, ERWIN Tool,SQL & Database Design Data Analysis & Data Profiling |Data Dictionary & Metadata Management Data Lineage and Impact Analysis Normalization & Data Modeling Best Practices
3. Desired Experience Range : 5 - 15 years
4. Location of Requirement : Hyderabad, Pune , Bangalore, Chennai

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Desired Competencies (Technical/Behavioral Competency)

1. 5+ years of experience in Data Modeling with expertise in Conceptual, Logical, and Physical Data Models
2. Hands-on experience with ERwin,
3. Strong proficiency in SQL, database design, and Data Warehousing concepts.
4. Experience in Source-to-Target Mapping, data profiling, and data analysis
5. Deep understanding of Metadata Management, Data Lineage, and Data Governance practices.

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• Experience with OpenMetadata, Collibra, Informatica EDC/Axon, or similar metadata management tools.
• Exposure to AWS, Snowflake, Databricks, or modern cloud data platforms.
• Knowledge of Data Governance, Data Quality, and Data Observability frameworks.
• Experience with Data Lineage, Impact Analysis, and Business Glossary Management.
• Understanding of API/JSON schema modeling and integration patterns.
• Familiarity with Data Lake, Big Data, and Analytics architectures
• Exposure to Agile delivery methodologies and stakeholder management in large-scale data transformation programs.
• Relevant certifications in Data Management, Cloud Technologies, or Banking Domain.

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Responsibility of / Expectations from the Role

1. Design and maintain Conceptual, Logical, and Physical Data Models aligned to business and enterprise data architecture standards.
2. Collaborate with business stakeholders, data architects, analysts, and engineering teams to translate business requirements into scalable data models.
3. Develop and maintain source-to-target mappings, data dictionaries, metadata, and business glossaries. Perform data analysis, profiling, lineage, and impact assessments to support data integration and transformation initiatives.
4. Ensure adherence to data governance, data quality, and regulatory compliance standards across banking data assets. Support enterprise metadata management and data catalog initiatives to improve data discoverability and usability.
5. Review and optimize database schemas for performance, scalability, and maintainability. Drive best practices in data modeling, documentation, and standards across project teams.
6. Work closely with cross-functional teams in an Agile environment to deliver data solutions that meet business and regulatory requirements.