Jyothsna Thondamanati

The projects focused on developing and testing a Single Sign-On (SSO) solution for Samsung’s web application and a student record management system for an education platform. Both projects involved rigorous testing of data integrity, database validation, and ensuring seamless integration between web interfaces and backend systems, including SQL Server databases.

Key Responsibilities:
1. Samsung SSO Web Application Testing

Performed functional and regression testing for Samsung's web application to ensure consistent software quality across multiple releases.

Collaborated with Agile teams to participate in sprint planning, user story discussions, story point estimation, and daily scrum meetings to align goals and deliverables.

Developed and maintained test scripts to optimize regression testing, reducing execution time and improving testing efficiency.

Validated data entered into the application by running SQL queries to ensure that all user authentication data, session records, and access permissions were correctly stored and mapped in the SQL Server database.

Tested the integration of the SSO solution, ensuring that authentication and session management data was accurately reflected in backend systems and databases.

Documented and reported test results, highlighting any discrepancies between the web interface and the database, providing actionable insights to the development team for quicker issue resolution.

2. Education System Student Record Management Testing

Developed a web interface for managing student records, enabling faculty and administrators to input and manage academic data for students, ensuring smooth data flow from front-end forms to backend databases.

Performed ETL testing to validate that student data entered through the web interface (e.g., student names, grades, courses) was accurately extracted, transformed, and loaded into SQL Server databases, ensuring data integrity across multiple tables (student, courses, grades).

Validated data mapping and relationships between multiple tables (student records, academic history, courses, and grades), ensuring correct foreign key associations and data consistency.

Executed SQL queries to validate data accuracy, checking that all data entered in the web interface was correctly reflected in the database and ensuring no discrepancies (e.g., missing records, incorrect grade entries).

Validated complex data flows from multiple forms (student details, grades, course enrollments) into the appropriate database tables, ensuring no data loss or incorrect transformations during the process.

Performed end-to-end testing of student record workflows, ensuring that data entered into the system was consistently accurate across tables and that database performance remained optimal under load.

Collaborated with database administrators to ensure that database indexes, queries, and relationships were optimized to handle large-scale student data with fast retrieval times.

Tested multi-user scenarios where multiple users interacted with the system simultaneously, ensuring no data conflicts occurred in concurrent updates to student records or session data.

Collaborated with cross-functional teams, including data engineers, backend developers, and database administrators, to resolve data inconsistencies, optimize data pipelines, and improve the efficiency of backend systems.

The project focused on enabling automated driving capabilities for unmanned Uber vehicles.

Key Responsibilities:

Prepared, cleaned, and mapped large-scale datasets to support machine learning model development for autonomous driving systems, ensuring high levels of data accuracy and consistency.

Collaborated with software engineers and data scientists to integrate real-time data streams into ML pipelines, driving continuous improvement of autonomous driving functions.

Performed comprehensive testing and optimization of core autonomous driving modules—including real-time tracking, object detection, path planning, and decision-making algorithms—to enhance safety, reliability, and overall system performance.

Identified, diagnosed, and resolved defects within self-driving system components, working closely with cross-functional teams to maintain stable and error-free operations.

Validated and assessed real-time tracking modules to ensure precise, responsive, and reliable vehicle localization across various operating conditions.

Supported the development and refinement of key algorithms for perception, planning, and control, contributing to ongoing product innovation and successful feature delivery.