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At Avenga, we believe that human creativity empowers technology that matters. Operating globally, our 6000+ specialists provide a full spectrum of services, including business and tech advisory, enterprise solutions, CX, UX and Ul design, managed services, product development, and software development.
This is the job
Our client is a leading tech company specializing in data-driven solutions for digital advertising and AI analytics. They provide scalable, high-performance data platforms for real-time decision-making, leveraging AI and machine learning to optimize marketing strategies.
They seek an experienced Automation QA Engineer to test high-volume data processing systems, ensure AI-driven solution reliability, and enhance automation frameworks.
The role focuses on automation but also requires some manual testing, specializing in Data/AI/ML, with the expectation to work in the client’s time zone at least until 2 PM PT (Pacific Time).
This is you
- 3–5+ years of experience in QA Engineering or SDET roles within data-driven or complex system environments.
- Ability to work in the client’s time zone at least until 2 PM PT (Pacific Time) — required.
- Solid programming and test automation skills using Python and SQL.
- Good understanding of testing data pipelines, data processing systems, and basic ML/AI workflows.
- Experience working with relational databases such as Postgres and familiarity with data warehouses (e.g., Snowflake).
- Knowledge of cloud platforms (AWS, GCP, or Azure) and their core services.
- Experience collaborating with cross-functional teams (Data Engineering, ML, Product).
- Good understanding of CI/CD processes and automated testing tools.
- Hands-on experience using version control systems such as GitHub or GitLab.
- Upper-Intermediate English level for daily communication.
- Contribute to QA efforts across AI/ML-based products and enterprise data platforms.
- Build and maintain automated tests using Python and SQL.
- Support the creation and execution of test plans for ML models, including accuracy and stability checks.
- Validate ETL processes and data pipelines across systems like Postgres and Snowflake.
- Work closely with Data Engineers, ML Engineers, and Product Managers to help define and track quality metrics.
- Perform functional, integration, and performance testing for data-intensive systems.
- Identify, log, and track defects, collaborating with engineering teams on resolution.
- Contribute to improving QA processes, automation practices, and CI/CD integration.
- Support team knowledge sharing and participate in building a culture of quality and continuous improvement.
Key Skills
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