Responsibilities
- Create and manage scalable data ingestion pipelines using ETL and ELT methods to bring data from various sources into Snowflake
- Build and optimize data warehouse components such as databases, tables, views, and materialized views within Snowflake
- Develop data transformation processes using SQL, dbt, Python, or comparable tools in Snowflake-based environments
- Configure and tune virtual warehouses in Snowface for optimal performance, concurrency, and cost management
- Utilize Snowflake capabilities like stages, tasks, streams, time travel, zero-copy cloning, and secure data sharing when applicable
- Process and load structured and semi-structured data formats including JSON, Avro, and Parquet
- Track query performance, resolve failures, and optimize workloads for stability and scalability in Snowflake
- Establish data quality checks, validation mechanisms, and monitoring systems for production data pipelines
- Collaborate with data analysts, BI developers, architects, and business teams to turn data needs into technical implementations
- Enforce data governance, security protocols, role-based access controls, and compliance standards in Snowflake environments
- Support continuous integration and deployment workflows for automated data engineering processes
- Document technical designs, data models, Snowflake objects, and operational procedures
