Company description
Tremend is looking for a skilled Data QE to join our team of bright thinkers and doers. You&rsquoll use your problem-solving creativity to figure out our client&rsquos 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&ndash7 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).
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