Snowflake Data Engineer

Key Skills:

Roles and Responsibilities:

• Design, develop, and maintain scalable data pipelines and ELT workflows using Snowflake SQL and DBT.
• Utilize SnowSQL CLI and Snowpipe for real-time and batch data loading, including the creation of custom functions and stored procedures.
• Implement Snowflake Task Orchestration and schema modeling, and perform system performance tuning for large-scale data environments.
• Build, deploy, and manage robust data models within Snowflake to support reporting and analytical solutions.
• Leverage DBT (CLI and Cloud) to script and manage complex ELT logic, applying best practices for version control using GitHub.
• Independently design and execute innovative ETL and reporting solutions that align with business and operational goals.
• Conduct issue triaging, pipeline debugging, and optimization to address data quality and processing gaps.
• Ensure technical designs adhere to data governance policies, security standards, and non-functional requirements (e.g., reliability, scalability, performance).
• Provide expert guidance on Snowflake features, optimization, security best practices, and cross-environment data movement strategies.
• Create and maintain comprehensive documentation for database objects, ETL processes, and data workflows.
• Collaborate with DevOps teams to implement CI/CD pipelines involving GitHub, DBT, and Snowflake integrations.
• Troubleshoot post-deployment production issues and deliver timely resolutions.

Experience Requirements:

• 5-8 years of experience in data engineering, with a strong focus on Snowflake and modern data architecture.
• Hands-on experience with Snowflake's architecture, including SnowSQL, Snowpipe, stored procedures, schema design, and workload optimization.
• Extensive experience with DBT (CLI and Cloud), including scripting, transformation logic, and integration with GitHub for version control.
• Successfully built and deployed large-scale ELT pipelines using Snowflake and DBT, optimizing for performance and data quality.
• Proven track record in troubleshooting complex production data issues and resolving them with minimal downtime.
• Experience aligning data engineering practices with data governance and compliance standards.
• Familiarity with CI/CD pipelines in a cloud data environment, including deploying updates to production using GitHub actions and DBT integrations.
• Strong ability to communicate technical details clearly across teams and stakeholders.

Education: