- Design, develop, and maintain scalable data pipelines using Python and modern data engineering tools. - Understanding of banking data models, financial reporting, and regulatory compliance. - Integrate data from legacy banking platforms into modern data lakes and warehouses. - Collaborate with business analysts, architects, and operations teams to understand core banking workflows and data requirements. - Design, build, and maintain scalable and reliable data pipelines using Azure Data Factory, Databricks, Synapse (Azure SQL DW), and Azure Data Lake. - Perform data analysis and transformation using PySpark on Azure Databricks or Apache Spark. - Design and implement data models optimized for storage and various query patterns. - Work with structured, semi-structured, and unstructured data. - Utilize various database technologies including Traditional RDBMS, MPP, and NoSQL. - Ensure compliant handling and management of data. - Experience in building end-to-end data pipelines in a cloud environment. - Mentor junior engineers and contribute to the development of best practices and reusable frameworks. Skills Expected: - 8+ years of relevant IT experience in the BI/DW domain with hands-on experience on the Azure up-to-date data platform. - Strong knowledge and experience with the Python programming language - Experience using version control systems like Git, bitbucket. - Experience in data analysis and transformation using PySpark on Azure Databricks or Apache Spark. - Good knowledge of Distributed Processing using Databricks or Apache Spark. - Experience in creating data structures optimized for storage and various query patterns. - Experience of integrating Unity Data Catalog with the Databricks and registering data assets etc. - Understanding of CI/CD pipelines. - Knowledge of Agile/Scrum methodologies.