CCRI-POPROX/poprox-storage
Mar 2025 – Jul 2025
2 Months active
Languages Used
Python
Technical Skills
Backend DevelopmentDatabaseDatabase InteractionDatabase ManagementSQLData Retrieval
Mehtab "Shahan" Iqbal
PROFILE
Mehtab "shahan" Iqbal
Mehtab Iqbal developed and enhanced backend data management features in the CCRI-POPROX/poprox-storage repository, focusing on survey and login data workflows. Over two months, Mehtab implemented analytics-ready retrieval methods for survey assignments and account-scoped access to login and Qualtrics survey responses, using Python and advanced SQL query design. The work included upsert-based storage for Qualtrics surveys to ensure data integrity, modular helper functions for efficient query execution, and targeted WHERE-clause filtering to improve data quality and privacy. These contributions improved data consistency, maintainability, and analytics readiness, demonstrating depth in backend development, database interaction, and repository pattern organization.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
5Total
Bugs
0
Commits
5
Features
4
Lines of code
86
Activity Months2
Your Network
16 peopleSame Organization
@clemson.edu6
Work History
July 2025
2 Commits • 2 Features
Jul 1, 2025Summary for 2025-07: Delivered two targeted data retrieval features in CCRI-POPROX/poprox-storage, enabling account-scoped access to login data and Qualtrics survey responses. Implemented a private helper to streamline query execution and mapping for login retrieval, increasing maintainability and reducing data processing overhead. Added a robust WHERE-clause-based method to fetch clean survey responses for specified accounts, improving data quality and selectivity for account-level analytics. No major bugs reported this month. These changes enhance data privacy, accuracy, and performance, supporting targeted analytics and compliance workflows. Technologies demonstrated include Python repository patterns, private helper functions, SQL query construction, and maintainable code organization.
2 Commits • 2 Features
Jul 1, 2025Summary for 2025-07: Delivered two targeted data retrieval features in CCRI-POPROX/poprox-storage, enabling account-scoped access to login data and Qualtrics survey responses. Implemented a private helper to streamline query execution and mapping for login retrieval, increasing maintainability and reducing data processing overhead. Added a robust WHERE-clause-based method to fetch clean survey responses for specified accounts, improving data quality and selectivity for account-level analytics. No major bugs reported this month. These changes enhance data privacy, accuracy, and performance, supporting targeted analytics and compliance workflows. Technologies demonstrated include Python repository patterns, private helper functions, SQL query construction, and maintainable code organization.
July 2025
March 2025
3 Commits • 2 Features
Mar 1, 2025Month: 2025-03 — CCRI-POPROX/poprox-storage delivered key features for survey data management and experimental analytics, with no major bugs fixed this month. Key achievements include implementing latest-survey retrieval by a specific set of IDs with an optional WHERE clause, introducing upsert-based Qualtrics survey storage to ensure data integrity, and enabling analytics-ready retrieval of all assignments by experiment by resolving group IDs. These changes improve data accuracy, consistency, and visibility into experiment results, enabling faster decision-making and better customer insights. Technologies demonstrated include SQL query design, upsert storage patterns, and modular data-access methods, with commits providing traceability.
March 2025
3 Commits • 2 Features
Mar 1, 2025Month: 2025-03 — CCRI-POPROX/poprox-storage delivered key features for survey data management and experimental analytics, with no major bugs fixed this month. Key achievements include implementing latest-survey retrieval by a specific set of IDs with an optional WHERE clause, introducing upsert-based Qualtrics survey storage to ensure data integrity, and enabling analytics-ready retrieval of all assignments by experiment by resolving group IDs. These changes improve data accuracy, consistency, and visibility into experiment results, enabling faster decision-making and better customer insights. Technologies demonstrated include SQL query design, upsert storage patterns, and modular data-access methods, with commits providing traceability.
Activity
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Quality Metrics
Correctness84.0%
Maintainability84.0%
Architecture84.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
Backend DevelopmentData RetrievalDatabaseDatabase InteractionDatabase ManagementSQL
Repositories Contributed To
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