Snowflake Data Engineer

Overview

• Design, develop, and maintain scalable ETL/ELT pipelines in Snowflake to support data migration from legacy systems.
• Leverage Python for data transformation, automation, and orchestration of migration workflows.
• Optimize and refactor complex SQL queries to ensure efficient data processing and reporting in Snowflake.
• Collaborate on data modeling and schema design to align with Snowflake architecture and performance best practices.
• Monitor and troubleshoot data pipeline performance during and after migration phases.
• Work closely with data analysts, scientists, and business stakeholders to ensure accurate and timely data delivery.
• Implement and enforce data governance, security policies, and access controls within Snowflake.
• Collaborate with DevOps teams to integrate data engineering workflows into broader CI/CD frameworks.
  
  

• 4–6 years of experience in data engineering, with proven expertise in Snowflake and Python.
• Strong command of Snowflake features such as scripting, time travel, virtual warehouses, and query optimization.
• Hands-on experience with ETL tools, data integration strategies, and migration methodologies.
• Solid understanding of data warehousing principles, normalization techniques, and performance optimization.
• Familiarity with cloud platforms (AWS, Azure, or GCP) and orchestration tools.
• Excellent problem-solving skills and ability to work independently in a dynamic, fast-paced environment.
• Understanding of version control systems, particularly Git, including branching, merging, and pull request workflows
• Experience with Snowflake advanced features including Snowpipe, Streams, Tasks, and Stored Procedures.
• Familiarity with ETL orchestration tools such as Airflow, DBT, or Matillion.
• Ability to work with semi-structured data formats like JSON and Parquet.