Senior Data Engineer/ Data Architect (Retail Banking Digital Transformation)

- Bachelor’s degree in Computer Science, Data Science, Information Systems, Software Engineering, or equivalent practical experience. - Strong background in Data Architecture, Data Engineering, Data Warehouse, or enterprise-scale data platforms. - Strong understanding of Data Architecture principles, Enterprise Data Architecture, Data Integration Architecture, and modern data platform architectures. - Strong knowledge of Data Warehouse, Data Lake, Lakehouse, Data Mart, Operational Data Store, and database design principles. - Strong hands-on experience in data modeling, including Conceptual Data Model, Logical Data Model, and Physical Data Model. - Solid understanding of dimensional modeling, normalized data models (3NF), and enterprise data modeling techniques. - Hands-on experience with Databricks (Cloud-based) or Oracle Data Warehouse environments for designing enterprise data solutions (mandatory requirement). - Experience in relational databases and data platforms, including Oracle, SQL Server, MySQL, and DB2 (DB2 is highly preferred). - Strong understanding of data integration patterns, including Batch, CDC, API-based integration, Event-driven, Near Real-time, and Streaming. - Experience in designing end-to-end data flows from source systems through ingestion, storage, transformation, and consumption layers. - Experience with Cloud platforms (AWS / Azure / GCP) and cloud-based data architectures. - Ability to translate business requirements and business capabilities into scalable data architecture and data models. - Ability to review technical designs, identify architectural risks and trade-offs, and provide clear recommendations. - Strong analytical thinking, structured problem-solving, and ability to work with complex enterprise environments. - Strong communication and stakeholder management skills, with the ability to collaborate across business, architecture, engineering, infrastructure, and vendor teams. - Experience with Agile Software Development and a solid understanding of Agile principles, Scrum methodology, and collaborative delivery models. - Team player with a proactive attitude and willingness to continuously learn and self-develop. Nice to Have (Strong Plus): - Experience or knowledge of IBM Banking Data Model or other enterprise banking data models. - Experience with Data Vault 2.0, including Raw Vault, Business Vault, PIT, and Bridge structures. - Experience with Databricks Lakehouse Architecture, Unity Catalog, and Medallion Architecture. - Understanding of banking data domains such as Customer, Account, Product, Transaction, Finance, Risk, and Regulatory Reporting. - Experience with Data Governance concepts and tools, including Business Glossary, Metadata Management, Data Lineage, Data Quality, and Data Ownership. - Understanding of DataOps practices, including CI/CD, automated testing, monitoring, logging, and data quality automation. - Experience with Enterprise Architecture frameworks or methodologies such as TOGAF. - Experience working with large-scale data transformation or legacy Data Warehouse modernization programs.

- Bachelor’s degree in Computer Science, Data Science, Information Systems, Software Engineering, or equivalent practical experience. - Strong background in Data Architecture, Data Engineering, Data Warehouse, or enterprise-scale data platforms. - Strong understanding of Data Architecture principles, Enterprise Data Architecture, Data Integration Architecture, and modern data platform architectures. - Strong knowledge of Data Warehouse, Data Lake, Lakehouse, Data Mart, Operational Data Store, and database design principles. - Strong hands-on experience in data modeling, including Conceptual Data Model, Logical Data Model, and Physical Data Model. - Solid understanding of dimensional modeling, normalized data models (3NF), and enterprise data modeling techniques. - Hands-on experience with Databricks (Cloud-based) or Oracle Data Warehouse environments for designing enterprise data solutions (mandatory requirement). - Experience in relational databases and data platforms, including Oracle, SQL Server, MySQL, and DB2 (DB2 is highly preferred). - Strong understanding of data integration patterns, including Batch, CDC, API-based integration, Event-driven, Near Real-time, and Streaming. - Experience in designing end-to-end data flows from source systems through ingestion, storage, transformation, and consumption layers. - Experience with Cloud platforms (AWS / Azure / GCP) and cloud-based data architectures. - Ability to translate business requirements and business capabilities into scalable data architecture and data models. - Ability to review technical designs, identify architectural risks and trade-offs, and provide clear recommendations. - Strong analytical thinking, structured problem-solving, and ability to work with complex enterprise environments. - Strong communication and stakeholder management skills, with the ability to collaborate across business, architecture, engineering, infrastructure, and vendor teams. - Experience with Agile Software Development and a solid understanding of Agile principles, Scrum methodology, and collaborative delivery models. - Team player with a proactive attitude and willingness to continuously learn and self-develop. Nice to Have (Strong Plus): - Experience or knowledge of IBM Banking Data Model or other enterprise banking data models. - Experience with Data Vault 2.0, including Raw Vault, Business Vault, PIT, and Bridge structures. - Experience with Databricks Lakehouse Architecture, Unity Catalog, and Medallion Architecture. - Understanding of banking data domains such as Customer, Account, Product, Transaction, Finance, Risk, and Regulatory Reporting. - Experience with Data Governance concepts and tools, including Business Glossary, Metadata Management, Data Lineage, Data Quality, and Data Ownership. - Understanding of DataOps practices, including CI/CD, automated testing, monitoring, logging, and data quality automation. - Experience with Enterprise Architecture frameworks or methodologies such as TOGAF. - Experience working with large-scale data transformation or legacy Data Warehouse modernization programs.