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# Remote, Data Quality Engineer

- **Company:** Insight Global
- **Location:** Pomona, CA, United States
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Data Analysis, Microsoft Azure, Continuous Integration, Data Validation, Extract Transform Load (ETL), Python (Programming Language), Windows PowerShell, Regression Testing, Power BI, Standard Sql, SQL Databases, Systems Integration, Data Logging, Scripting, Azure Data Factory, Snowflake, Data Analytics, Data Pipelines
- **Published:** June 8, 2026
- **Apply:** https://dejobs.org/x/x/0411C62F86A44145AE5D2513A6CADB33/job/

## About the Role

* 4-7 years of experience in a Data Quality, Data QA or Data Tester role.
 * Experience performing SQL-based data validation in Snowflake using complex joins, aggregations, and window functions.
 * Experience with source-to-target testing across ELT pipelines, validating transformations and business rules.
 * Experience performing regression testing to protect downstream reports and analytics
 * Experience logging defects and collaborate with Data Engineers and Analytics teams to resolve data issues.
 * Experience working in an Azure cloud environment, with a solid understanding of Azure data and integration services.
 * Experience using Python and/or PowerShell for data validation or scripting.
 * Experience validating data used in Power BI reports or dashboards.
 * Must be able to work Pacific Standard Time hours.

## Description

Insight Global is seeking a Data Quality Engineer for a large enterprise client operating within a modern Azure-based cloud data ecosystem. This individual will play a critical role in ensuring the quality, integrity, performance, and security of Snowflake data warehouse solutions. The engineer will partner closely with data engineers, BI teams, and product owners to validate complex ETL/ELT pipelines, automate data quality checks, and support CI/CD deployments. This role is ideal for a detail-oriented QA professional with strong SQL, Snowflake, and Azure experience who thrives in enterprise-scale, data-driven environments.


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