Untitled

In my work on the COBOL Mainframe Programming project, I focused on thoroughly understanding the COBOL syntax and logic to ensure alignment with business requirements and data. Despite being new to COBOL, I proactively researched and learned the language’s rules and semantics, collaborating with our mainframe expert to identify the different types of COBOL statements.

I took full ownership of addressing any missed cases, which was critical to avoiding rework. This involved not only rigorously researching every corner of COBOL’s rules but also continuously asking insightful questions to verify whether specific scenarios were valid, required, or potentially overlooked. By consistently seeking expert validation and proactively investigating any gaps in the solution, I ensured that all edge cases were addressed, and the solution was both comprehensive and adaptable to future changes.

Additionally, I made sure to build Python logic that covered all possible COBOL rules, anticipating any future changes or new requirements. This proactive approach was instrumental in minimizing rework, as it ensured the solutions were robust, scalable, and adaptable to evolving needs. It allowed us to stay ahead of potential challenges and reduced the risk of major revisions in the future.

For side projects like CPBRE and PI Evaluation Report Generation, I made it a point to fully understand the requirements upfront, ensuring alignment with the business needs and minimizing the risk of rework. By clarifying expectations early, I was able to deliver the results in a timely and efficient manner, meeting the needs of the project stakeholders without unnecessary revisions.

By ensuring a strong alignment between the business need, data, and the technical solution, I significantly reduced the risk of rework, ensuring efficiency and accuracy throughout the project.