Key Responsibilities • Design, develop, and optimize data pipelines using Snowflake and ETL/ELT frameworks. • Write complex SQL queries, stored procedures, and performance-optimized transformations. • Build and maintain scalable data ingestion and transformation workflows. • Implement data warehouse solutions, data modeling, and data integration processes. • Work with Python and PySpark for data processing and transformation. • Optimize Snowflake query performance, warehouse utilization, and compute costs. • Integrate data from multiple sources, including cloud storage, databases, and APIs. • Implement data quality checks, error handling, and monitoring. • Collaborate with data architects, analysts, and business stakeholders. • Follow best practices for data security, governance, and access management. Mandatory Skills • Snowflake: Hands-on experience with Snowflake data warehouse, SQL, and query optimization. • SQL: Strong experience writing complex queries, joins, CTEs, window functions, and performance tuning. • ETL/ELT: Experience developing and maintaining data pipelines. • Python: Data processing, transformation, and automation. • Data Modeling: Experience with dimensional modeling, schema design, and data warehouse concepts. • Cloud Data Platforms: Experience with AWS, Azure, or GCP. Good-to-Have Skills • PySpark / Apache Spark • Databricks and Delta Lake • Apache Airflow or other workflow orchestration tools • Snowflake Streams, Tasks, Snowpipe, and Dynamic Tables • Snowflake performance tuning and cost optimization • CI/CD, Git, and automated testing • Cloud storage and data lake integration