Data Engineer

Role & Responsibilities Design, develop, and optimize scalable ETL pipelines using Python, SQL, Snowflake, and Azure technologies. Build, maintain, and enhance enterprise data warehouse and lakehouse solutions with a focus on performance, scalability, and reliability. Develop complex SQL queries, data models, and database solutions to support business and analytics requirements. Implement and manage data integration workflows using tools such as Azure Data Factory (ADF), Databricks/Spark, Informatica, or SnapLogic. Ensure data quality, governance, monitoring, and production support for data pipelines and platforms. Optimize query performance and troubleshoot data-related issues in production environments. Collaborate with cross-functional teams to design and deliver robust data solutions. Support batch and real-time/streaming data processing requirements. Preferred Candidate Profile 5-9 years of experience in Data Engineering, Data Warehousing, or ETL Development. Strong hands-on experience with Snowflake, ETL processes, Python, and Advanced SQL (mandatory). Experience working with Azure data services, including Azure Data Factory (ADF), Databricks, Azure Storage, and Event Hub . Solid understanding of data modeling, database design, query optimization, and performance tuning . Experience with relational databases such as Oracle, SQL Server, MySQL, and PostgreSQL . Exposure to CI/CD pipelines, GitHub, Azure DevOps, and DataOps practices . Experience with data integration tools such as Informatica or SnapLogic . Knowledge of batch and streaming data pipeline architectures. Strong analytical, troubleshooting, and problem-solving skills. Ability to work in a fast-paced environment and support enterprise-scale data platforms.