Qa Lead

QA Lead

You will be responsible for validating incoming sensor data, leading a small team of QA engineers, and coordinating manual and automated testing of new features and enhancements. The ideal candidate has experience with end-to-end QA processes for data-rich dashboards and a strong grasp of test automation and UI testing.

Key Responsibilities:

Sensor Data & Dashboard Validation

• Validate customer data displayed on dashboards against raw sensor inputs.
• Design and execute test cases to verify data accuracy, transformation logic, and visual representation.
• Collaborate with the data engineering team to define validation rules and thresholds for data anomalies.

Feature and UI Testing

• Conduct comprehensive testing of new UI features before release.
• Define and maintain test plans, test cases, and test scripts for new functionality.
• Perform exploratory, regression, and usability testing as needed.

Automation & Tools

• Evaluate, implement, and maintain automated testing frameworks (e.g., Selenium, Cypress, Playwright).
• Build automated test suites for both frontend and backend services.
• Integrate test suites into CI/CD pipelines for continuous delivery confidence.

Team Leadership

• Mentor and lead a small QA team (13 members) with a focus on ownership and best practices
• Collaborate with product, engineering, and DevOps teams to ensure holistic quality across the stack.

Required Skills & Qualifications:

• Bachelor's degree in Computer Science, Engineering, or related field.
• Proven experience in validating

  sensor data, IoT inputs, or telemetry systems.

• Strong understanding of QA methodologies, tools, and processes.
• Hands-on experience with test automation frameworks and scripting languages (Python, JavaScript, etc.).
• Familiarity with APIs, Postman, RESTful services, and data inspection tools (e.g., Kibana, Grafana, SQL).
• Excellent communication and organizational skills.

Nice-to-Have:

• Experience with time-series databases or MQTT brokers.
• Knowledge of cloud platforms (AWS, Azure, GCP) and DevOps pipelines.
• Exposure to machine condition monitoring or industrial IoT environments.