Who are we looking for:

We are looking for a motivated Quality Assurance (QA) Engineer with a focus on data intensive applications and specifically on tabular data (i.e. CSV / Excel / Parquet / Text data).This role requires a keen eye for detail and a robust understanding of QA methodologies to ensure that our synthetic data solutions meet the highest standards of quality and reliability. Your expertise will be instrumental in validating the functionality and performance of our synthetic data platform.

Key Responsibilities:

• Test Case Development and Execution: Craft detailed test plans and cases. Conduct hands-on testing of our web applications and tabular data scenario handling, including manual and automated testing for CSV / Excel / Parquet / Text data.
• Performance and Load Testing: Utilize performance and load testing tools to simulate high usage and analyze the scalability and resilience of our systems. Identify potential bottlenecks and work with development teams to address these issues.
• Data Quality Assurance: Implement stringent testing protocols to ensure the accuracy, completeness, and consistency of synthetic data generated by our platform. Verify that data transformation and integration meet predefined standards and user expectations.
• Collaboration with Development Teams: Work closely with software developers and data scientists to understand system functionalities and integration points. Provide feedback and recommendations based on test outcomes to enhance system performance and usability.
• Continuous Improvement: Advocate for and implement QA best practices and new technologies. Continuously seek to improve testing strategies and methodologies to align with evolving project requirements and industry standards.

Essential Skills and Qualifications:

High priority

• Minimum of 2 years of experience in software testing in data intensive applications.
• Demonstrated experience with testing tools and methodologies relevant to load, performance, and functional testing.
• Strong analytical skills and problem-solving capabilities with meticulous attention to detail and a curiousity to learn.
• Proven ability to work effectively both independently and within a collaborative team environment.
• Good understanding of data integrity, data validation, and transformation testing.

Good to have

• Familiarity with Python for writing test scripts and automating tasks.
• Proficiency in SQL and experience with database testing to validate data consistency and integrity.
• Experience in a data science or machine learning context, particularly with testing related to data processing.
• Excellent communication and interpersonal skills, capable of effectively articulating test findings and collaborating with technical and non-technical team members alike.