Chapter 1. Setting the Stage
This project focused on building a backend processing system used to determine store incentive eligibility based on recurring operational performance data. The application runs periodically, evaluates records using business rules, and helps operational teams identify which stores qualify for incentives in a more consistent and automated way.
This work was delivered in collaboration with Internal Development Team.
Chapter 2. Carrying the Work
Responsibilities in this project included Requirement Analysis, Backend Processing Development, Database Logic Implementation, Data Processing and Transformation, Periodic Job Handling, and Testing and Validation.
Chapter 3. What Changed
The project helped simplify incentive calculation by processing large volumes of operational data automatically and classifying the results into eligible and non-eligible groups. It reduced manual work, improved consistency, and created a more reliable foundation for reporting and follow-up operational processes.
Chapter 4. The Problem and the Response
Problem
The business process needed a more dependable way to decide which stores qualified for incentives based on repeated operational evaluations. A manual approach would take more time and increase the risk of inconsistency, so this project introduced a backend processing flow that could evaluate data automatically and generate structured results for the business team.
Solution
My main contribution was on the core backend and database-side processing logic. I focused on how the data was prepared, processed, and classified during each execution cycle so the system could produce accurate and repeatable outcomes.
Chapter 5. How It Was Built
This project centered around a periodic backend processing module that evaluates operational data and determines incentive eligibility for stores. The system was designed to support a repeatable evaluation cycle and help the business team review results more efficiently.
Implementation Flow: Backend processing logic handles validation, transformation, and structured execution of the evaluation flow. The system processes recurring data in a consistent way so the same rules can be applied reliably across each period. The output is stored in a structured format that supports downstream business usage such as review, reporting, and follow-up processes.
Implementation details included Backend data processing system, Python-based processing flow, PostgreSQL-based data handling, Rule-based evaluation pipeline, Recurring execution flow for periodic evaluation, Supported deployment to Google Cloud with assistance from the research team, Structured output for reporting and operations, and Testing and validation to maintain processing accuracy.
Some system names, data structures, and implementation details have been intentionally generalized because this was an internal enterprise project.
Chapter 6. Application Flow
Primary Flow: Operational performance data is collected and prepared as the main input for the incentive evaluation cycle. The system applies predefined business rules to determine whether each record meets the incentive criteria. The results are classified into eligible and non-eligible groups, then prepared for operational review and reporting.
