QA Engineer - Data-Driven API & ETL Automation (Azure)

Gazelle Global; Polska; On-site; PLN 330,000 - 428,000; Full time

Job summary

Gazelle Global is seeking a QA Engineer to strengthen our data-intensive applications. The role combines test automation, API/backend testing, data validation, and ETL testing within an Agile/Scrum environment.

You will design and maintain automated test suites for APIs and data pipelines, conduct manual and regression testing, validate ETL processes and data quality, and collaborate with developers and data engineers to improve testing coverage and quality across environments.

Qualifications

• Strong knowledge of SQL and database testing.
• Proficient in Python for test automation.
• Experience with API and backend testing.
• Experience with ETL testing and data validation.

Responsibilities

• Design, develop, and maintain automated test suites for APIs, backend services, and data pipelines.
• Perform manual, functional, regression, integration, and release testing.
• Validate ETL processes, data transformations, data quality, and database outputs.
• Create and execute test cases based on business and technical requirements.
• Support release activities and deployment validation across environments.
• Analyze test results, investigate defects, and collaborate with developers to identify and resolve issues.
• Contribute to the improvement of QA processes, automation coverage, and testing frameworks.
• Work closely with cross-functional teams to identify quality risks throughout the software development lifecycle.

Skills

SQL

Python

Microsoft Azure

API testing

Data validation

ETL testing

Agile/Scrum

QA best practices

Tools

Azure Data Factory

Databricks

GitLab

CI/CD pipelines

Job description

Gazelle Global is seeking a QA Engineer to strengthen our data-intensive applications. The role combines test automation, API/backend testing, data validation, and ETL testing within an Agile/Scrum environment.

You will design and maintain automated test suites for APIs and data pipelines, conduct manual and regression testing, validate ETL processes and data quality, and collaborate with developers and data engineers to improve testing coverage and quality across environments.