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Fractal Analytics • Noida, Uttar Pradesh, India
Role & seniority: Senior QA Engineer (Data Management & Platforms)
Stack/tools: Python, Databricks, Azure; PySpark; API testing; test automation frameworks (Python BDD); Docker; CI/CD; Agile environment
Lead/participate in QA teams, manage daily activities, training, and mentoring; define and maintain QA processes for cloud/data engineering
Develop test plans, scenarios, cases, and data; own end-to-end QA automation and manual testing; coordinate test case execution and defect management
Collaborate with Product Owners, UX/UI, and Development Leads to define testing roadmaps and quality standards across projects; oversee releases
3–5 years software testing experience; 8+ years in QA roles
Strong Python proficiency; experience testing PySpark and APIs
Experience with test automation (Python BDD), CI/CD integration, Docker
Knowledge of QA methodologies, DevOps, Agile; excellent stakeholder communication
Location & work type: Location not specified; work type not explicitly stated (likely full-time)
It's fun to work in a company where people truly BELIEVE in what they are doing! We're committed to bringing passion and customer focus to the business. Fractal is a strategic AI partner to Fortune 500 companies with a vision to power every human decision in the enterprise. Fractal is building a world where individual choices, freedom, and diversity are the greatest assets. An ecosystem where human imagination is at the heart of every decision. Where no possibility is written off, only challenged to get better. We believe that a true Fractalite is the one who empowers imagination with intelligence. Fractal has been featured as a Great Place to Work by The Economic Times in partnership with the Great Place to Work® Institute and recognized as a ‘Cool Vendor’ and a ‘Vendor to Watch’ by Gartner. QA Engineer - Data Management & Platforms The Quality Assurance Engineer will join our team and support the deployment and monitoring of quality standards. The QA Engineer will work with engineers across teams to develop a Quality Assurance framework for the entire organization. As a QA Engineer you will be a part of the Data Management & Platforms team and will help drive quality throughout the entire data product lifecycle through collaboration with product and development teams, development of test cases and automation scripts, and analysis of the system to consistently deliver quality products to downstream consumers Requirements: Python+Databricks+Azure Primary Responsibilities: Lead and participate within QA teams through management of daily activities, training, and mentoring Collaborate with stakeholders to improve quality standards and testing procedures Develop test plans, test scenarios, test cases and test data for different testing activities Guide testing methodologies across the teams on all different projects Provide definition, implementation, and maintenance of standard QA processes and methodologies for cloud, data, and application engineering efforts Collaborate with Product Owners and UX/UI Designers teams to understand different use cases and develop test plans accordingly Collaborate with Product and Development Leads to develop and implement a testing roadmap for the product Work with development teams to develop quality standards for areas such as test automation, unit, functional, system integration, performance, and user acceptance test phases Own end-to-end QA automation and manual quality assurance processes and execution Define and implement KPIs to support end-to-end automation in the product development quality life cycle Coordinate and manage platform releases by overseeing test case execution, documenting defects, and managing deployment of releases Troubleshoot quality issues and modify test procedures when applicable Education: Bachelors degree in a relevant field such as Information Systems, Computer Science, Engineering, etc. Required Experience and Skills: 3 to 5 years of software testing experience 8+ years of experience working as a QA Engineer or in a similar capacity Knowledge of QA methodologies, tools, and techniques Knowledge of DevOps principals Experience working in an Agile environment Excellent communication skills and ability to work with the different stakeholders to improve the company’s procedures and to ensure the vision is communicated clearly Previous experience as test automation engineer at various development stages and integrating with CI/CD pipelines (e.g., unit, integration, load tests) Proficiency in Python programming Experience with testing PySpark code and API testing Experience with test automation frameworks (Python BDD) Experience with docker containers Analytical thinking, problem-solving skills, and attention to detail Preferred Experience and Skills: QA management experience Experience working with professional software engineering practices and best practices for the full software development life cycle Experience improving product test coverage and effectiveness If you like wild growth and working with happy, enthusiastic over-achievers, you'll enjoy your career with us! Not the right fit? Let us know you're interested in a future opportunity by clicking Introduce Yourself in the top-right corner of the page or create an account to set up email alerts as new job postings become available that meet your interest! At Fractal, towards our goal of “powering every human decision in the enterprise”, our partnerships and alliances help in creating and delivering a compelling suite of solutions to unlock value. We partner with companies from around the globe, leaders in their respective fields. With Fractal’s expertise in artificial intelligence, design, engineering, and digital transformation, combined with the data, technology, and software platforms from our partners, we create cutting-edge solutions to problems in the business world. We understand how critical and timely decision triggers, and information, empower our clients to create, unlock, deliver, and realize value. Together with our partners, our goal is to serve each client in their end-to-end data-to-decision journey.