**Company description** Tremend is looking for a skilled Data QE to join our team of bright thinkers and doers. You’ll use your problem-solving creativity to figure out our client’s most complex and challenging problems across different industries. You should be insightful, inquisitive, and naturally collaborative, with a strong desire to share ideas and opinions. **Overview** + Design scalable and maintainable test automation frameworks tailored for data-intensive applications and analytical pipelines + Collaborate with architects, developers, and data engineers to ensure testability, quality, and performance of analytics solutions + Drive the adoption of best practices in test automation architecture and continuous integration/continuous testing + Develop reusable libraries and components to streamline test creation and execution + Lead technical discussions and mentor junior QE team members in automation and tooling + Partner with cross-functional teams to define automation strategies across various layers of the application and data stack. **Responsibilities** + Design scalable and maintainable test automation frameworks tailored for data-intensive applications and analytical pipelines + Collaborate with architects, developers, and data engineers to ensure testability, quality, and performance of analytics solutions + Drive the adoption of best practices in test automation architecture and continuous integration/continuous testing + Develop reusable libraries and components to streamline test creation and execution + Lead technical discussions and mentor junior QE team members in automation and tooling + Partner with cross-functional teams to define automation strategies across various layers of the application and data stack. **Qualifications** + 5–7 years of experience in test automation with at least 2 years in architecting automation frameworks. + Deep understanding of data validation, ETL testing, and data quality frameworks. + Proficiency in at least one programming/scripting language python, pyspark, sql + Experience testing in data analytics environments (e.g., BigQuery, Snowflake, Databricks). + Working knowledge of API testing, mocking tools, and test data management strategies. + Exposure to containerization (Docker) and cloud environments (AWS, GCP, or Azure).