Snowflake Lead Data Engineer

About the opportunity

We are seeking a highly skilled and experienced Snowflake Developer with a strong background in SQL, Python, and a minimum of 3 years of hands-on experience with Snowflake. The ideal candidate will be a Snowflake Certified Consultant with a proven track record in data warehousing, data modeling, and implementing ETL/ELT pipelines using industry-standard tools. You will play a key role in building scalable and efficient data solutions that support business intelligence and analytics initiatives.

Primary Roles & Responsibilities

• Design, develop, and optimize data pipelines and ETL/ELT processes in Snowflake.
• Develop and optimize complex SQL queries for data extraction, transformation, and reporting.
• Write robust Python scripts for automation, orchestration, and data transformations.
• Migrate data from legacy systems to Snowflake and integrate various data sources.
• Implement Snowflake best practices for performance tuning, security, and cost management.
• Collaborate with cross-functional teams to implement end-to-end data warehouse solutions.
• Ensure data quality, reliability, and compliance with data governance policies.
• Participate in code reviews, design sessions, and provide mentorship to junior developers.

Required Skills

• 8+ years of total experience in Data Engineering / Development.
• Minimum 3 years of hands-on experience with Snowflake Snowflake Certified (Mandatory).
• Strong expertise in SQL development and optimization.
• Proficient in Python for scripting and data engineering tasks.
• Experience in Data Warehouse architecture and Data Modeling (Star/Snowflake Schema).
• Hands-on experience with ETL/ELT tools like Informatica, Matillion, dbt, Talend, or equivalent.
• Experience with cloud platforms (AWS, Azure, or GCP) and associated services.
• Solid understanding of performance tuning, data governance, and security concepts.
• Excellent problem-solving and communication skills.

Preferred Skills:

• Experience with CI/CD pipelines and DevOps practices in data engineering.
• Exposure to Data Lake, Delta Lake, or other modern data architecture patterns.
• Familiarity with tools like Airflow, Snowpipe, or Kafka is a plus.