TheRole:

QA Data Specialist - Software Development Engineer in Test II

The Team:

Our central data and reporting function serve as the organizational hub for all data spanning financials to inventory, uniquely positioning us to innovate and build cutting-edge data products and AI/ML solutions for internal stakeholders. We foster a collaborative environment that values experimentation, data-driven decision making, and continuous learning, where team members regularly share knowledge about the latest AI developments and methodologies. The team maintains a culture of intellectual curiosity where experienced practitioners mentor team members and provide meaningful projects that contribute to real-world applications.

The Impact:

As a Quality Engineer managing AI systems, you will play a crucial role in developing and optimizing testing frameworks that utilize AI algorithms to enhance the testing process across Web/Mobile/API/Services. Your challenge will be to reduce the time to market for products while ensuring quality, by implementing AI-driven test optimization techniques. You will work with a variety of advanced technologies and collaborate with different internal teams.

What's in it for you:

Collaborate with a team of highly skilled, ambitious, and result-oriented professionals focused on AI advancements.
Utilize cutting-edge AI technologies to innovate testing methodologies and enhance automation.
Experience a dynamic environment that allows you to think and act like a developer while fulfilling QA responsibilities.
Engage in a culture that promotes urgency and proactive approaches in quality assurance.
Opportunities for skill enhancement, knowledge sharing, and innovation in AI-driven testing solutions.
Build a rewarding career with a global leader in financial technology.

Responsibilities:

Design and develop comprehensive test strategies for data engineering pipelines and AI-driven systems, ensuring data quality, integrity, and reliability across platforms such as Databricks, Microsoft Fabric, and Power BI, while implementing automated regression suites that are maintainable and reusable.
Validate data transformations, ETL/ELT processes, and data workflows by creating test cases that verify data accuracy, completeness, and consistency across source systems, data lakes, and analytical platforms, utilizing SQL, Python, or PySpark for data validation.
Implement AI algorithms and machine learning techniques to analyze testing scenarios, optimize test case selection, and enhance test coverage for data pipeline testing, reducing time-to-market while maintaining quality standards.
Collaborate with data engineering, development, and business intelligence teams in an Agile environment to ensure timely delivery of data solutions and business functionalities that meet quality acceptance criteria, participating in sprint planning, technical reviews, and requirement analysis sessions.
Develop and execute performance testing strategies for data pipelines and Power BI reports, monitoring data processing efficiency, query performance, and dashboard responsiveness to ensure optimal user experience and system scalability.
Provide technical guidance to the development team around QA inner workings, automation frameworks, and AI-enhanced testing methodologies, while gathering quality metrics to drive continuous improvement initiatives and risk mitigation strategies.

What We're Looking For:

Required Qualifications:

3+ years of experience in software testing or development with deep understanding of testing methodologies, coding, and debugging, including hands-on experience with data quality testing and validation across data engineering platforms.
Proficiency in programming languages such as Python, Java, or C#, with strong SQL/PL-SQL skills for database interactions and data validation, and experience with big data processing languages such as PySpark or Scala for testing data pipelines.
Hands-on experience with data engineering and analytics platforms such as Databricks, Microsoft Fabric, or Azure Data Factory, and business intelligence tools like Power BI, Tableau, or similar visualization platforms for testing dashboards and reports.
Experience designing and developing automated testing frameworks using tools such as AccelQ, Tosca, Selenium, Pytest, or Great Expectations, with ability to create test automation for multiple layers including UI, API, Services, and data validation.
Strong understanding of ETL/ELT processes, data warehousing concepts, and data lake architectures, with ability to validate data transformations, data quality rules, and end-to-end data pipeline workflows.
Familiarity with Agile/Scrum methodologies, CI/CD practices, and version control systems such as Git, GitHub, or Azure DevOps, with experience working in collaborative, fast-paced development environments.

Preferred Qualifications:

Experience with AI-driven testing tools and platforms, including machine learning frameworks such as TensorFlow, PyTorch, or scikit-learn, with demonstrated ability to implement intelligent test optimization and predictive analytics for quality assurance.
Knowledge of cloud platforms such as Azure, AWS, or GCP, with experience in testing cloud-native data solutions, serverless architectures, and containerization technologies such as Docker, Kubernetes, or Azure Container Instances.
Exposure to data quality frameworks and tools such as Apache Griffin, Deequ, or Monte Carlo, with understanding of data observability practices, data lineage tracking, metadata management, and experience with data pipeline technologies including Databricks Delta Live Tables (DLT) for ensuring end-to-end data quality.
Experience with Behavior Driven Development (BDD) frameworks such as Cucumber or SpecFlow, performance testing tools like JMeter or Locust, and familiarity with API testing tools such as Postman, REST Assured, or SoapUI.