Lead Backend Engineer

I designed, built, and deployed a stock trading GraphQL API in Amazon ECS, managing the entire lifecycle of the project from initial concept to production. I started by gathering requirements and understanding the needs of the trading platform, which involved close collaboration with stakeholders to ensure that the API would meet both performance and security expectations. I decided on GraphQL due to its flexibility in handling complex queries and its efficiency in data retrieval, which is critical in the fast-paced environment of stock trading.

Once the requirements were clear, I architected the API with scalability and robustness in mind. I carefully planned the data models to represent various trading entities such as stocks, orders, transactions, and user portfolios. I implemented resolvers to handle complex business logic and ensure that each query or mutation would be processed accurately and quickly. The design also incorporated authorization and authentication mechanisms to protect sensitive financial data and ensure that users could only access their own information.

For the backend, I chose to deploy the API on Amazon ECS to leverage container orchestration, scalability, and the reliability of AWS infrastructure. I containerized the application using Docker, ensuring that the development, testing, and production environments were consistent. This approach streamlined the deployment process and made it easier to manage dependencies and updates. I configured the ECS cluster, defined task definitions, and set up services to maintain high availability and load balancing. I also integrated AWS Fargate to manage container scaling automatically, which allowed the API to handle fluctuating trading volumes without manual intervention.

During development, I focused on thorough testing and continuous integration. I wrote comprehensive unit and integration tests to validate the API’s functionality, ensuring that all edge cases were addressed and that the system could handle the expected load. I used a CI/CD pipeline to automate the build, test, and deployment processes, which reduced downtime and minimized the risk of introducing bugs into production. This pipeline was configured to deploy the latest version of the API to ECS, ensuring that any updates were rolled out smoothly and could be quickly rolled back in case of issues.

Security was a top priority throughout the project. I implemented encryption for data in transit and at rest, and I ensured that the API adhered to industry best practices for securing sensitive financial information. I set up monitoring and logging using AWS CloudWatch to track performance metrics, error rates, and usage patterns. This monitoring allowed me to quickly identify and address any issues, ensuring that the API remained reliable even under heavy load.

After deployment, I continued to optimize the API based on real-world usage and feedback. I refined the schema and resolvers to further improve response times and reduce latency. I also worked on scaling strategies to manage increased trading volumes during peak market hours, ensuring that the system remained responsive even during periods of high demand.

In the end, the project resulted in a robust, scalable, and secure GraphQL API that supported the trading platform’s needs. It provided efficient, real-time data access and transactional capabilities, enabling users to execute trades and manage their portfolios seamlessly. This project not only demonstrated my technical expertise in API design and containerized deployment but also showcased my ability to work with cloud technologies like Amazon ECS to deliver enterprise-grade solutions in a dynamic and demanding environment.