AI Summary
AppIQ Technologies seeks a meticulous and strategic QA Engineer to ensure the quality and reliability of its Machine-Learning-driven e-commerce funnel optimisation and digital advertising platform. The ideal candidate will have 4+ years of experience in software quality assurance or engineering, with a strong focus on scalable web applications. They will be responsible for defining the testing strategy, prioritizing bug fixes, and collaborating with developers and data scientists to ensure a seamless end-user experience.
Key Highlights
Define testing strategy for high-performance applications
Prioritize bug fixes based on risk of failure and potential impact
Collaborate with developers and data scientists to ensure a seamless end-user experience
Technical Skills Required
Benefits & Perks
Competitive compensation
Generous paid time off
Work-from-anywhere flexibility
Opportunities to develop expertise in building cutting-edge predictive AI applications
Job Description
AppIQ Technologies is seeking a meticulous and strategic QA Engineer / Sr. QA Engineer to ensure the quality and reliability of our Machine-Learning-driven e-commerce funnel optimisation and digital advertising platform.
You will be responsible for defining the testing strategy for high-performance applications that leverage our proprietary Predictive AI solutions.
As a key member of our fast-paced startup, you will balance the need for rapid feature deployment with the necessity of thorough testing. You will be responsible for identifying and prioritising the highest-risk bugs to ensure our scalable services, which manage millions of daily events, remain robust and accurate.
- QA Architecture & Strategy: Develop and maintain a comprehensive QA architecture that supports full-stack applications and complex microservices.
- Risk Management: Prioritise bug fixes based on risk of failure and potential impact, while striking a productive balance between speed-to-market and exhaustive testing.
- Test Management: Utilise test case management (TCM) systems such as TestRail, Zephyr, Xray, PractiTest, qTest, or similar to organise test cases, track execution, and provide transparent reporting on quality metrics.
- Automated Testing: Design, implement, and scale automated test suites using tools such as Playwright, Cypress, and Appium.
- Testing & Validation: Perform rigorous unit tests and integration tests on applications built with TypeScript, React, Node.js, Python, and PySpark.
- Infrastructure Testing: Verify the reliability of deployments across AWS (EC2, S3, Firehose) and Cloudflare edge environments.
- Data Integrity: Collaborate with Data Engineers to validate the accuracy of complex event data and real-time reporting dashboards.
- Cross-Functional Collaboration: Act as a great team player with excellent communication skills, working closely with developers and data scientists to ensure a seamless end-user experience.
- 4+ years of professional experience in software quality assurance or engineering, with a strong focus on scalable web applications (7+years for Sr. QA Engineer).
- Strong grasp of QA architecture and modern testing methodologies.
- Deep expertise in the tech stack used by our engineers, specifically TypeScript, React, Node.js, Python, and PySpark.
- Cloud & Database Proficiency: Familiarity with AWS services and both SQL and NoSQL (e.g., MongoDB) databases to effectively test data persistence and performance.
- Global Collaboration: Ability to work effectively with globally distributed teams
- Native or Business-level proficiency in written and spoken English
- AI/ML Literacy: Understanding of Machine Learning (Supervised/Reinforcement Learning), Predictive AI, and the validation of Data Pipelines.
- Proficiency in Python or experience with PySpark.
- Prior experience in the e-commerce or Ad Tech ecosystem (DSPs, Audience Data, Fraud detection).
- The opportunity to shape the QA culture and architecture from the ground up.
- An attractive career path on either a management or an individual contributor track.
- Genuine learning, training and development opportunities, supported by regular performance reviews
- Competitive compensation and generous paid time off.
- Work-from-anywhere flexibility
- Opportunities to develop expertise in building cutting-edge predictive AI applications.
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