Role Overview :

We are looking for an experienced ETL Testing Lead / Data QA Lead to lead data quality and ETL testing initiatives within an Agile environment. The ideal candidate will have strong hands-on experience in ETL testing, SQL, Snowflake, DBT, data validation, data quality, and data lineage, along with the ability to mentor and guide QA teams.

Experience working with US Healthcare data is highly preferred.

The candidate will collaborate closely with Data Engineering, Product, Business, and QA teams to ensure the accuracy, completeness, consistency, and reliability of enterprise data pipelines.

Key Responsibilities :

- Lead and mentor ETL/Data Testing teams and provide technical guidance within Agile squads.

- Define and implement ETL/Data Testing strategies, test plans, test scenarios, and test cases.

- Validate end-to-end data pipelines, data transformations, and source-to-target mappings.

- Perform complex SQL queries for data validation, reconciliation, and defect analysis.

- Validate data transformations and pipelines using Snowflake, SnowSQL, Snowpipe, and Stored Procedures.

- Test DBT models and transformations and validate data quality across different layers of the data warehouse.

- Ensure data accuracy, completeness, consistency, integrity, and reliability.

- Validate and document data lineage from source systems through transformation to target systems.

- Develop and execute data quality checks using tools/frameworks such as Great Expectations.

- Identify data anomalies, transformation issues, and pipeline defects and coordinate with engineering teams for resolution.

- Integrate testing activities with CI/CD pipelines and support automated data validation.

- Work with Git/Bitbucket for source control, branching, code reviews, and test asset management.

- Collaborate with Engineering, Product, Business, and other QA teams to resolve complex data-related issues.

- Review and improve testing documentation, processes, and quality standards.

- Track defects, testing progress, risks, dependencies, and quality metrics.

- Drive continuous improvement and automation of repetitive data testing activities.

- Communicate complex technical issues and data-quality findings effectively to both technical and non-technical stakeholders.

Mandatory Skills :

- 7+ years of experience in ETL Testing / Data Testing / Data QA.

- Strong hands-on expertise in SQL and database validation.

- Strong experience with Snowflake and Snowflake-based data platforms.

- Hands-on experience with SnowSQL, Snowpipe, and Stored Procedures.

- Strong understanding of ETL/ELT processes and data warehouse concepts.

- Experience with DBT/dbt and SQL-based data transformations.

- Strong understanding of data quality, data validation, data reconciliation, and data lineage.

- Experience with CI/CD processes and testing integration.

- Experience with Git/Bitbucket.

- Proven experience leading, mentoring, or guiding ETL/Data QA teams.

- Strong understanding of Agile/Scrum methodologies.

- Excellent analytical, problem-solving, and communication skills.

Preferred / Good-to-Have Skills :

- Experience in the US Healthcare domain.

- Experience with Great Expectations or similar data-quality frameworks.

- Experience with Python for data validation or test automation.

- Experience with Airflow or other data orchestration tools.

- Exposure to AWS/Azure cloud platforms.

- Experience with Databricks or other modern data platforms.

- Knowledge of healthcare data, claims, member, provider, or related healthcare datasets.

- Knowledge of HIPAA and healthcare data privacy/compliance is an advantage.

Education :

- Bachelor's or Master's degree in Computer Science, Information Technology, Engineering, or a related field.

What We Are Looking For :

We are looking for someone who combines strong technical ETL/Data Testing expertise with QA leadership capabilities. The ideal candidate should be comfortable working hands-on with SQL and Snowflake while also guiding a team, defining testing approaches, solving complex data-quality issues, and collaborating with cross-functional stakeholders.