Senior Snowflake Data Engineer — Remote Data Modernization

Cornerstonetechtalent; Austin (TX); Remote; USD 150,000 - 190,000; Full time

Job summary

CornerStone Technology Talent Services seeks a Senior Snowflake Data Engineer to modernize legacy data extracts into cloud-based pipelines and analytics. You will transform complex source systems, optimize Snowflake solutions, and enable trusted business reporting via Power BI.

You will work across the data lifecycle—from reverse engineering data to validating Snowflake loads and supporting semantic models. Strong collaboration with data engineers and stakeholders is essential.

Qualifications

• 8+ years of hands-on Snowflake experience with SQL development and performance tuning.
• 4+ years in ETL/ELT concepts, data pipelines, and data integration patterns.
• 5+ years analyzing complex data flows and troubleshooting across systems.

Responsibilities

• Design, develop, and maintain ETL/ELT pipelines into Snowflake.
• Reverse engineer data extracts and document source-to-business mappings.
• Develop and optimize Snowflake SQL, loading processes, and transformations.
• Collaborate on data modeling, schema design, and transformation logic.
• Validate mappings, reconcile results, and investigate discrepancies.
• Support Power BI datasets and dashboards using Snowflake as the data source.
• Troubleshoot data flows end-to-end from source to reporting.
• Document data flows, metadata, and procedures for maintainability.

Skills

Data modeling

Documentation

Cross-functional collaboration

Problem solving

Analytical thinking

Education

Bachelor's degree in CS or related field

Tools

Snowflake

SQL

ETL/ELT

Power BI

Informatica

Matillion

Azure Data Factory

Job description

CornerStone Technology Talent Services seeks a Senior Snowflake Data Engineer to modernize legacy data extracts into cloud-based pipelines and analytics. You will transform complex source systems, optimize Snowflake solutions, and enable trusted business reporting via Power BI.

You will work across the data lifecycle—from reverse engineering data to validating Snowflake loads and supporting semantic models. Strong collaboration with data engineers and stakeholders is essential.