Experience: Minimum 4-6 years of experience Strong experience in data cleaning and transformation using tools such as SQL, Python (Pandas), or R to ensure data accuracy and consistency. Hands-on experience in building and maintaining ETL/ELT pipelines using technologies like SSIS, AWS DMS, AWS Glue, Python, AWS Lambda, ECS, EventBridge, or Spring. Good knowledge of database design and experience working with databases such as PostgreSQL, MySQL, MongoDB, Cassandra, SQLite, Athena, and AWS S3. Experience working with cloud platforms such as AWS, Azure, or Google Cloud. Experience developing production-grade data pipelines and data integration solutions in big data environments. Understanding of system design, data structures, and algorithms. Familiar with data architecture concepts including Data Lakes, Data Warehouses, Data Marts, and Data Virtualization for efficient data storage and access.