Data QA

Nexite is at the forefront of developing a pioneering connectivity platform that transforms the interaction between users and physical items by digitalizing them. This innovation propels retail into a new era—data-driven, seamless, and personalized. Our ecosystem comprises state-of-the-art battery-less tags, fixed-mounted IoT devices, and sophisticated cloud-based management platform.

Nexite's platform revolutionizes item tracking and inventory management—offering live, minute-to-minute in-store inventory insights, fitting room analytics, secure and swift self-checkout processes, personalized in-store IoT experiences, and post-sale engagement with a Smart Closet feature for consumers. Recognized among the "50 Most Promising Israeli Startups of 2023" by Calcalist, Nexite is shaping the future of retail and connectivity. Learn more: Calcalist Tech

We are looking for a passionate and meticulous Data QA to join our Data Research & Development team. This role focuses on ensuring the accuracy, quality, and robustness of our data and algorithms while driving innovation to enhance our platform’s capabilities.

• Develop and execute comprehensive testing frameworks to validate data pipelines, transformations, and analytics.
• Identify, report, and resolve data inconsistencies, anomalies, and inaccuracies across complex datasets.
• Collaborate with data engineers and analysts to establish best practices for data validation and monitoring.
• Automate QA processes to ensure consistent data accuracy at scale.
• Investigate innovative techniques for improving data integrity and system reliability.
• Monitor real-world usage data to refine and enhance algorithms.
• Identify edge cases and ensure robustness of the platform against them.

• Bachelor’s degree in Computer Science, Data Science, Engineering, or a related field.
• 4+ years of experience in data QA, analytics, or a related discipline.
• Proficiency in SQL for database validation and experience testing APIs.
• Familiarity with manual and automated testing techniques.
• Strong understanding of QA methodologies, tools, and processes. (Ability to create and maintain test cases, test plans, and test scripts)
• Strong analytical skills for debugging and isolating issues.
• Experience with data processing frameworks and visualization tools.
• Knowledge of scripting languages (like Python) for creating test scripts. 
• Familiarity with machine learning workflows and model validation.
• Proven ability to design and implement automated QA workflows.
• Experience working with large, complex datasets in a SaaS environment.
• Knowledge of cloud platforms (e.g., AWS, GCP, Azure) and containerized workflows (e.g., Docker, Kubernetes).
• Understanding statistical testing, hypothesis validation, and A/B testing.