Data QA Automation Engineer

About Streamhub

The Role

Responsibilities and Required Skills

• Use JavaScript or Python to contribute to full-stack modules, including React, API development, and data modeling.
• Develop and maintain API and web automation frameworks using Cypress, Playwright, Selenium, or similar tools.
• Design automated validation frameworks for ETL pipelines and large-scale data systems.
• Validate transformations, aggregations, and business rules across high-volume datasets.
• Automate data quality, end-to-end testing, and agent evaluation workflows.
• Use generative AI for test generation, coverage improvement, anomaly detection, data drift detection, and root cause analysis.
• Integrate validation and quality gates into CI/CD pipelines for regression, schema, and contract checks.
• Perform advanced SQL and database validation testing, including reconciliation and data integrity checks.

Preferred Skills

• Hands-on experience with AI-assisted development tools such as Cursor or similar.
• Experience with performance testing tools such as JMeter or Gatling.
• Knowledge of additional programming languages such as Python, Java, or Scala.
• Familiarity with distributed data systems, event-driven architectures, or large-scale data platforms.

Package

• The package includes competitive salary, learning opportunities, exposure to niche data technologies, and work with terabytes of data.
• The team offers an open, diverse, autonomous, and entrepreneurial culture with flexible timing.

Requirements

• Strong hands-on coding skills in JavaScript or Python.
• Ability to contribute to full-stack modules, including React, Python, API development, and data modeling, as the role expands.
• Experience developing and maintaining API and web automation frameworks such as Cypress, Playwright, Selenium, or similar.
• Experience designing and maintaining automated validation frameworks for ETL pipelines and large-scale data systems.
• Experience validating complex data transformations, aggregations, and business rules across high-volume datasets.
• Experience automating data quality, end-to-end testing, and agent evaluation workflows.
• Experience integrating automated validation and quality gates into CI/CD pipelines.
• Advanced SQL and database validation testing experience, including reconciliation and data integrity checks.
• Hands-on experience with AI-assisted development tools such as Cursor or similar.
• Experience with performance testing tools such as JMeter or Gatling.
• Knowledge of additional programming languages such as Python, Java, or Scala.
• Familiarity with distributed data systems, event-driven architectures, or large-scale data platforms.