We are seeking a highly skilled and motivated Data QA Engineer to join our Data Insights department. In this role, you will work closely with cross-functional agile teams and data scientists to deliver projects focused on analyzing and leveraging data. You will play a crucial role in ensuring the accuracy, reliability, and quality of our data-driven insights, while working within an iterative process to tackle projects with ambiguous requirements.
As a Data QA Engineer, you will be responsible for designing and implementing automated testing strategies that enable efficient and effective quality assurance of data products. Your expertise will be instrumental in identifying and preventing data anomalies, verifying data transformation processes, and ensuring data integrity throughout the project lifecycle.
- Minimum of 3 years or more of experience in running and maintaining automated test cases, with a focus on data testing, data validity, and information architecture.
- Expertise in Data Testing: Strong understanding of data testing principles, methodologies, and best practices.
- Experienced in designing and executing test scenarios to validate data integrity, accuracy, and completeness.
- Automation Skills: Proficiency in developing and maintaining automated test scripts using Appium, Selenium, unirest/Rest Assured for APIs and Cucumber. Familiarity with CI/CD tools such as Jenkins/Bitrise or any other.
- SQL and Database Knowledge: Solid understanding of SQL and experience in querying and manipulating data in relational databases. Ability to write complex SQL queries to validate data integrity and perform data validations across multiple data sources.
- Good communication skills in English to effectively communicate with various stakeholders.
※Please note that our main coding language for backend/server side is Java. During the QA selection process, you will have a coding challenge.
- ETL (Extract, Transform, Load) and machine learning model testing experience. Familiarity with testing data pipelines, monitoring and alerting on model accuracy, and detecting model drift, among other tasks.
- Data analytical skills or introductory-level statistical modeling.
- Regulatory Compliance: Understanding of industry-specific regulations and experience in validating data compliance with regulatory requirements.
- Ability to learn new technologies and set up new integrations with various data sources such as data lakes, object stores, and queues.
- Experience in running performance tests using Gatling or similar tools.
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