What are the responsibilities and job description for the Data QA Lead - AI position at Commergence?
Job Title: Data QA Lead – AI exp
Location: San Ramon, CA
Duration: 12 Months
Role Overview
We are seeking an experienced Data QA Lead with strong expertise in Data Testing, AI/ML validation, and enterprise data quality initiatives. The ideal candidate will lead QA efforts for large-scale data platforms, AI-driven applications, and cloud-based analytics solutions while ensuring data accuracy, integrity, and reliability across complex ecosystems.
Key responsibilities include defining QA strategy, developing test plans, validating ETL/ELT pipelines, performing data reconciliation, and ensuring data quality across structured and unstructured datasets. The candidate should have hands-on experience validating AI/ML models, feature engineering datasets, model outputs, and data pipelines supporting predictive analytics and Generative AI use cases.
The role requires strong expertise in SQL, data warehousing, API testing, automation frameworks, and cloud data platforms such as Snowflake, Databricks, Azure, or AWS. Experience with big data technologies, PySpark/Spark, metadata validation, and data governance concepts is highly preferred.
The candidate will collaborate closely with Data Engineers, Data Scientists, Architects, Product Owners, and Business teams to support end-to-end testing activities including SIT, UAT, regression testing, and production validation within Agile delivery environments. Strong analytical, troubleshooting, and communication skills are required.
Skills: data qa lead,artificial intelligence,data quality
Location: San Ramon, CA
Duration: 12 Months
Role Overview
We are seeking an experienced Data QA Lead with strong expertise in Data Testing, AI/ML validation, and enterprise data quality initiatives. The ideal candidate will lead QA efforts for large-scale data platforms, AI-driven applications, and cloud-based analytics solutions while ensuring data accuracy, integrity, and reliability across complex ecosystems.
Key responsibilities include defining QA strategy, developing test plans, validating ETL/ELT pipelines, performing data reconciliation, and ensuring data quality across structured and unstructured datasets. The candidate should have hands-on experience validating AI/ML models, feature engineering datasets, model outputs, and data pipelines supporting predictive analytics and Generative AI use cases.
The role requires strong expertise in SQL, data warehousing, API testing, automation frameworks, and cloud data platforms such as Snowflake, Databricks, Azure, or AWS. Experience with big data technologies, PySpark/Spark, metadata validation, and data governance concepts is highly preferred.
The candidate will collaborate closely with Data Engineers, Data Scientists, Architects, Product Owners, and Business teams to support end-to-end testing activities including SIT, UAT, regression testing, and production validation within Agile delivery environments. Strong analytical, troubleshooting, and communication skills are required.
Skills: data qa lead,artificial intelligence,data quality