Data QA Engineer

Vietnam

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Not SpecifiedCompensation

Mid-level (3 to 4 years), Senior (5 to 8 years)Experience Level

Full TimeJob Type

UnknownVisa

Data Engineering, Cloud Data PlatformsIndustries

Requirements

Proven experience in data-focused QA roles, preferably within cloud-based data ecosystems, is required. Candidates must possess strong SQL skills for data analysis and validation, experience testing pipelines and scripting with Python, and familiarity with Snowflake, AWS Data Lake services (S3, Glue, Athena), or similar platforms. A working knowledge of test automation, including experience with Playwright or similar frameworks like Cypress or Selenium, is essential, along with a solid understanding of both backend data workflows and front-end interfaces. Familiarity with data orchestration tools like Airflow or dbt, experience with CI/CD pipelines, exposure to data governance or compliance requirements, and experience testing data APIs or dashboards are considered nice to have.

Responsibilities

The Data QA Engineer will design and execute test cases to validate data pipelines and transformations in Snowflake and AWS Data Lake environments. They will perform data validation, reconciliation, and integrity checks using SQL and custom scripts, and test Python-based ETL/ELT pipelines, ensuring accuracy, error handling, and performance. This role involves implementing and maintaining automated testing frameworks, with a focus on Playwright for UI components, and collaborating with data engineers and platform teams to test infrastructure setup, configuration, and access control. Responsibilities also include logging and triaging defects, supporting teams through resolution and release, and contributing to QA strategies and test plans.

Skills

Data Quality Assurance

Data Pipelines

ETL/ELT

SQL

Python

Snowflake

AWS Data Lake

Glue

Athena

Test Automation

Playwright

Cypress

Selenium

Data Validation

Data Reconciliation

Data Integrity

Backend Data Workflows

Front-end Interfaces

Dashboards

Data Visualization

Mineral

Develops AI tools for sustainable agriculture

About Mineral

Mineral.ai develops technology solutions aimed at improving the agriculture industry. The company utilizes perception technology, artificial intelligence (AI), and machine learning (ML) to create tools that help farmers, researchers, and agricultural advisors increase crop yields, manage pests, and adapt to climate change. Their products include precision agriculture tools that optimize resource use and advanced data analytics platforms that provide insights from agricultural data. Unlike many competitors, Mineral.ai focuses on creating partnerships within the agriculture sector to co-develop solutions, enhancing their product offerings. The goal of Mineral.ai is to support sustainable food production and help feed the world more efficiently.

Mountain View, CaliforniaHeadquarters

N/AYear Founded

VENTURE_UNKNOWNCompany Stage

Food & Agriculture, AI & Machine LearningIndustries

51-200Employees

Risks

Transition from Alphabet may lead to financial instability and resource loss.

Licensing model could reduce control over technology application and revenue stability.

Difficulty in monetizing technology indicates challenges in creating durable revenue streams.

Differentiation

Mineral.ai uses AI and ML to revolutionize agriculture with precision tools.

The company partners with industry leaders like Driscoll's for real-world technology applications.

Mineral.ai's licensing model allows broad integration into existing agribusiness systems.

Upsides

Licensing model increases market reach and technology integration in agribusiness.

Partnerships with companies like Driscoll's enhance technology application and sustainability goals.

Growing interest in agrivoltaic systems offers new partnership opportunities for Mineral.ai.

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