As a Data Engineer on the Data Management Platform (DMP) team, I was responsible for building large-scale data pipelines using batch processing to enable audience segmentation. These pipelines significantly contributed to increasing the CPM (Cost per Mille) prices of ad slots, enhancing the company's advertising effectiveness and revenue. The data pipelines were developed on Databricks using Python and PySpark. I also wrote and optimized ETL scripts in Python to ensure seamless data integration and processing. Throughout my role, I leveraged my strong technical expertise in the Databricks platform to meet the company's evolving data engineering needs.

I contributed to building the Amagi Automation Framework (AAF) aimed at reducing manual testing efforts for Amagi Cloudport. My key contributions include developing Python-based database utility modules, integrating legacy services using FastAPI, and creating health-check APIs for microservices using Kubernetes and Argo workflows. Additionally, I worked on AWS-based media asset ingestion pipelines and wrote comprehensive automated tests for various test cycles using Playwright and Pytest. My work enhanced test automation processes, including automated reporting via Slack notifications.

I developed a strong foundation in backend development with a focus on building robust and scalable APIs. Leveraging the Spring Framework with Hibernate as the ORM tool, I designed and implemented efficient backend solutions. I gained experience with the Java Persistence API (JPA), working extensively with PostgreSQL as the primary database system. Additionally, I wrote comprehensive test suites for APIs using Postman, ensuring thorough testing and debugging to deliver high-quality, reliable backend services.