Job Title

Job Description: database systems that process millions of financial transactions daily. You will work at the intersection of big data processing and database excellence, ensuring our platform meets stringent performance, reliability, and scalability requirements.Key Responsibilities:•Design, build, and maintain scalable ETL data pipeline using ADLS Gen2, Azure Blob storage, Azure Databricks, Python, Azure SQL Database and Azure Cosmos DB.•Perform deep database performance tuning, query optimization, and indexing strategies across Azure SQL Database and Cosmos DB•Lead database migration initiatives, including strategic migration from Azure SQL Database and CosmosDB to PostgreSQL•Implement efficient data models and manage data storage solutions using ADLS Gen2 and real time data streaming using ADF, Spark and Confluent Kafka.•Develop and maintain data quality checks, monitoring, and alerting systems. Ensure data solutions comply with regulatory and security standards (e.g., PCI DSS)•Optimize batch processing workflows for intra-day and end-of-day banking operationsRequired Qualifications:•10 years of hands-on experience in data engineering with strong database expertise.•At least 5 years of experience in implementing operational data store (ODS) for the retail banking space, which captures the multiple cross system transactions, providing high throughput and low latency solution using RDBMS or NoSQL Technologies.•Should have hands-on experience implementing solutions using ADLS Gen2, Azure Blob storage, Azure Databricks, Azure SQL Database and Azure Cosmos DB.•Should have hand on experience in writing queries using the Normalized Data model for the retail banking space. Expert-level skills in database performance tuning, query optimization, and execution plan analysis•Proven experience with large-scale database migrations (Azure SQL Database to PostgreSQL preferred)•Schema Migration Tools like Liquibase - Hands-on experience database migration tools managing version history and applying changes reliably. And managing schema changes with versioning, tracking, and rollback capabilities.•Strong programming skills in Python for data processing and automation•Experience with data modeling, ETL/ELT processes, and data warehouse concepts.•Excellent problem-solving skills with ability to diagnose and resolve complex data pipeline issues