Data QA

NTT DATA•Atlanta, GA

About The Position

The Senior QA Data Engineer is a strong individual contributor on the QA Data Engineering Team, responsible for complex test design and validation work. The role creates detailed test plans and cases for new transformations and ingestion processes, performs advanced data validation across base tables and tabular cubes, and leads root cause analysis of high-impact data quality failures. The position also contributes to Python and Databricks automation assets and mentors other QA Engineers and Testers. The role is based in Atlanta, Georgia. It involves working with development teams and business stakeholders to clarify requirements, assess the effects of transformations and migrations on downstream cubes and reports, and document data issue findings, impact, and remediation. The stated starting pay range is $60 to $70 per hour.

Requirements

5–7 years of QA experience, including at least 3 years focused on data warehouse, ETL/ELT, or pipeline testing.
5–7 years of hands-on testing experience with Snowflake and Databricks, including notebooks, SQL, and basic PySpark.
5–7 years of Python experience writing validation scripts, iterating across columns and tables, and producing automated comparison reports.
5–7 years of advanced SQL experience, including complex joins, aggregations, window functions, and reconciliation patterns.
5–7 years of demonstrated ability to independently write detailed test plans and test cases.
5–7 years of experience performing root cause analysis on data issues and producing structured RCA documentation.
5–7 years of experience working in Jira-driven QA workflows.
Strong communication skills and comfort working directly with business stakeholders to clarify requirements.

Nice To Haves

Experience with Azure Synapse or Azure Data Warehouse.
DAX query authoring against tabular cubes, such as SSAS Tabular, Azure Analysis Services, or Power BI.
Exposure to AI workflows or AI-assisted automation for test case generation or data validation.
Experience with synthetic and controlled test data generation.

Responsibilities

Create comprehensive test plans and test cases for new transformations and ingestion processes, working from minimal business-supplied input.
Validate code against base tables, check key-field correctness, and reconcile outputs against business and vendor expectations.
Write complex SQL and Python validation scripts to compare data warehouse sources with tabular cube outputs, including completeness, totals, null percentages, and key-field checks.
Author DAX queries against tabular cubes to extract data for comparison with the Azure Data Warehouse source.
Lead testing of new transformations and migrations across Snowflake and Databricks, assessing impacts on downstream cubes and reports.
Conduct root cause analysis on data quality incidents; document findings, impact, and remediation; and partner with development teams on resolution.
Contribute to and extend automated test scripts and frameworks under the Technical Lead's direction.
Use Jira to receive assignments and notifications, start test plans, and track results.
Mentor QA Engineers and Testers on SQL, Python, validation techniques, and test planning.
Work directly with business stakeholders to clarify requirements.