Dhruv Shah

Set up local development environment in VS Code and IntelliJ IDEA, configured Tomcat servers, Maven builds, and proxy configurations to connect Angular frontend to Java backend APIs Use Git for version control, work on main branch and follow the team's repository structure across multiple projects file structure Debug connection issues between the Angular frontend and Java backend , modify proxy.config.json files and restart development servers to establish proper API routing

Identified, diagnosed, and resolved software defects by analyzing code and using debugging tools. Worked closely with senior developers and team leaders to understand issues, implement fixes, and follow code review guidelines. Improved code quality and stability by fixing existing bugs, enhancing performance, and refactoring code where necessary. Managed bug fixes and code changes using version control tools ( Bitbucket).Utilized issue tracking systems (JIRA) to track bug progress and prioritize fixes. Documented root cause analyses, and steps taken to resolve issues for future reference.

Developed and executed manual and automated test cases based on functional and technical specifications. Participated in daily stand-up meetings, providing updates on testing progress and communicating issues to the team. Logged, tracked, and verified defects in bug-tracking tools such as JIRA, ensuring timely resolution. Preparing test plans, scripts, and reports and utilizing testing software(Selenium).Worked closely with senior QA engineers and developers to continuously improve the testing process and product quality.

Responsible for building the data infrastructure from the ground up, designing automated ETL pipelines that processed over 1 million monthly interactions, cutting manual reporting time from 32 to just 10 hours per week. I developed forecasting and statistical models using 3+ years of historical data to support demand planning, and built normalised relational datasets that standardised KPI definitions across business units, bringing consistency to how performance was measured company-wide. On the visualization side, I created interactive dashboards in Tableau and Power BI for 10–15 stakeholders, making data accessible to people with varying levels of technical background. I also worked closely with operations and marketing teams to translate real business problems into clear data requirements and measurable outcomes.

During this internship, I focused heavily on building predictive capabilities the business didn't yet have. I developed time-series forecasting models in Python to anticipate seasonal demand fluctuations, a critical need in the textile and dyeing industry where inventory mismanagement directly impacts margins. These models achieved 80%+ forecast accuracy, giving the operations team a reliable planning tool for the first time. A large part of my work involved getting the data itself in order. I cleaned and transformed multi-store retail datasets using SQL and Pandas, resolving inconsistencies that had long affected reporting reliability. This groundwork made downstream analysis significantly more trustworthy. Through exploratory data analysis, I uncovered sales patterns that led to a 14% improvement in inventory allocation, reducing both overstock and shortfall situations.