Data QA Lead

Indotronix International Corporation•Tempe, AZ

3d•Remote

About The Position

Join a high-impact data platform initiative as a Data Quality Assurance Lead. In this pivotal role, you’ll be hands-on with advanced data testing while also mentoring a small offshore team. From day one, you’ll shape the project’s architecture and quality standards, working with cutting-edge tools like Databricks, Snowflake, and Azure Data Factory. If you’re passionate about data quality and eager for leadership growth, this is your chance to drive excellence from the ground up—all in a fully remote setting.

Requirements

Bachelor’s degree in Computer Science, Information Technology, Engineering, or a related field
Minimum 7 years in Data QA, ETL/Data Warehouse Testing, or Data Quality Validation
At least 1 year of experience in a formal team leadership or mentorship role
Proven hands-on expertise in Snowflake, Databricks, Azure Data Factory, Azure Data Lake, and SQL
Advanced understanding of Data Warehouse, ETL/ELT testing, and source-to-target mapping validation
Outstanding communication skills—verbal and written

Nice To Haves

Python and PyTest automation experience
Familiarity with Snowflake Data Quality Monitoring and Data Metric Functions
Reporting validation using Power BI or similar tools
Knowledge of Data Governance and Data Quality frameworks
Experience with Azure DevOps, CI/CD pipelines, and transferable automation tools like Selenium

Responsibilities

Lead all QA activities for large-scale data engineering and analytics projects.
Design, implement, and execute robust test strategies, plans, and cases for data pipelines and platforms.
Validate data transformations, business rules, and complex source-to-target mappings across Databricks, Snowflake, and Azure Data Factory.
Perform comprehensive end-to-end data testing, including migration validation and reconciliation across multiple data layers.
Develop and maintain automated data validation and reconciliation frameworks.
Create and run complex SQL-based test scenarios for large datasets.
Conduct rigorous data quality checks: null validation, duplicate detection, row count verification, and referential integrity assessments.
Collaborate closely with Data Engineers, Architects, and business stakeholders as part of an Agile team.
Mentor and provide technical guidance to 2–3 junior QA team members.