Sr. QA Engineer - Machine Learning Platform for E-Commerce

AppIQ Technologies; Netherlands; Remote; EUR 50,000 - 70,000; Full time

Benefits offered by this job

Competitive compensation

Flexible work-from-anywhere policy

Career development opportunities

Generous paid time off

Learning and training support

Job summary

A dynamic tech startup is seeking a meticulous QA Engineer to ensure the quality of their Machine-Learning-driven platform. You will develop QA architecture, prioritize risk, and implement automated testing. Ideal candidates have over 4 years of QA experience and a deep understanding of TypeScript, React, Node.js, and Python, coupled with AWS familiarity. The role offers an attractive career path, competitive compensation, and work-from-anywhere flexibility, making it a great opportunity for tech professionals eager to shape QA culture.

Qualifications

• 4+ years of experience in software quality assurance or engineering.
• Strong grasp of modern testing methodologies.
• Deep expertise in TypeScript, React, Node.js, Python, and PySpark.

Responsibilities

• Develop and maintain QA architecture for full-stack applications.
• Prioritise bug fixes based on risk of failure.
• Utilise test case management systems for tracking.

Skills

QA architecture

Automated testing

Risk assessment

Team collaboration

QA methodologies

Tools

TestRail

Playwright

Cypress

AWS

Job description

Overview

AppIQ Tech 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 thorough testing and identify and prioritise the highest-risk bugs to ensure our scalable services, which manage millions of daily events, remain robust and accurate.

Responsibilities

• 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 balancing speed-to-market with 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.

Qualifications

• 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

Strong plus if you also have:

• 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).

Benefits

• 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.