Created and implemented dynamic pricing models using Python and SQL, & drove a 15% revenue increase by analyzing booking patterns, customer behavior, and seasonal demand fluctuations.Developed Power BI dashboards to display critical metrics such as booking trends, customer demographics, and revenue performance, empowering management to make informed decisions that enhanced operational efficiency.Administered and analyzed large-scale datasets with PySpark, delivered real-time insights into marketing campaign effectiveness, resulting in a 10% boost in customer engagement through targeted marketing initiatives.Built and optimized predictive models and SQL queries to forecast booking trends, & enhanced platform performance by 20% and reduced operational costs by 5% through better resource management.Reduced data processing time by 40% through the implementation of more efficient data extraction and cleaning techniques, improving team productivity.Improved data-driven decision-making by creating self-service data tools for non-technical teams, which resulted in a 30% increase in data adoption across departments.

Developed and deployed back-end systems for TriptoAnywhere using Python and Flask, automated booking management, transaction processing, and customer data handling, resulting in a 25% boost in operational efficiency.Engineered API integrations with MongoDB, optimizing data flow between the platform's back-end and database, enhancing application performance by 20% and reducing data retrieval latency.Designed and scheduled cron jobs to automate processes like fetching travel data and sending user notifications, cutting manual efforts by 30% while enhancing system reliability.Partnered with the Front-End Team using React Native to integrate back-end services, improved the overall user experience and built a more cohesive TriptoAnywhere platform.Leveraged PySpark for large-scale data processing to analyze customer behavior and travel booking patterns, driving marketing strategies and product development.