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
Worked on the CCRI-POPROX/poprox-storage repository to deliver four backend features focused on survey data management and targeted data retrieval. Developed Python modules leveraging SQL for robust database interaction, including upsert-based storage for Qualtrics surveys to ensure data integrity and analytics-ready retrieval of assignments by experiment. Introduced account-scoped access to login data and clean survey responses, using private helper functions to streamline query execution and improve maintainability. Emphasized modular code organization and traceable commits, enhancing data accuracy, privacy, and performance. The work supported analytics and compliance workflows by enabling selective, efficient access to experimental and account-level survey data.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
5Total
Bugs
0
Commits
5
Features
4
Lines of code
86
Activity Months2
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19 peopleShared Repositories
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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
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