Overview

During this role, the focus was on developing scalable and maintainable web solutions while improving deployment processes and overall development efficiency. A full-stack web application was built for an external client to analyze marketing data through an intuitive dashboard, enabling stakeholders to make more informed, data-driven decisions.

Later on, the focus shifted to the development of Sybyl, a customizable web application designed to analyze marketing data and generate future predictions using AI. Sybyl was developed to be tailored per client, adapting to unique data sets and business requirements to deliver actionable insights and forecast marketing trends. This transition marked a deeper integration of AI-driven features and emphasized scalable architecture to support multiple deployments.

To support internal development efforts, Node.js libraries were built and maintained to streamline workflows, reduce redundancy, and ensure consistency across multiple projects. Automated unit and integration tests were implemented to catch issues early and improve release confidence, reducing deployment-related problems.

Collaboration with senior developers was key to adopting and promoting best practices in software architecture, testing strategies, and deployment pipelines. Active participation in Agile ceremonies such as sprint planning and code reviews contributed to team alignment and iterative progress.

Scalable applications were deployed to Google Cloud Platform (GCP), using cloud-native services to ensure performance and uptime. Docker was used to containerize application components, simplifying development, testing, and deployment environments. CI/CD pipelines were designed and implemented using GitHub Actions to automate builds, testing, and deployments, accelerating the release process and reducing manual overhead.