Metehan Alan

As a Senior Backend Engineer at Lotto24 AG, I contribute to the development and maintenance of 10-15 microservices, primarily built using Kotlin and Java, leveraging frameworks like Micronaut and Spring Boot. Our backend systems utilize PostgreSQL and MongoDB to ensure efficient data storage and management. On the frontend, I support internal back-office applications powered by Vue.js, while TypeScript plays a key role in building robust automated test suites. Additionally, I integrate technologies like Apache Kafka for messaging and Datadog, Splunk for monitoring, ensuring scalability, reliability, and observability across our services. My role focuses on delivering scalable software solutions, enhancing system architecture, and fostering best practices in a collaborative, microservices-driven environment.

- Worked with more than 50 different microservices that have Kotlin and Java codebases. - Provided solid test bases that cover the whole codebases more than %95 by implementing Unit, Integration tests by using Junit Mockito, Wiremock and test containers. - Owned different projects and designed their system architectures and published the ADRs of them to our teams. - Migrated 5 different blocking flows to purely event driven flows. Utilised Kafka, RabbitMQ and AWS SQS/SNS on different use cases. - Increased our monitoring and alarming significantly by exposing prometheus metrics and provided visual boards on Grafana by using them. - Participated in the review of the colleague's code and provided feedbacks regularly. - Helped to maintain the readability and high standards of the code by creating and applying SonarQube lint rules.

- Worked in a project that about migrating large monolith banking system to microservice architecture. - Designed, developed new applications and components using Continuous Integration Development. - Worked with Java and variuous techologies including Spring, Hibernate, JDBC, Spring Batch, RestFUL web services. - Worked with Camunda BPM for creating workflow and decision models, operating deployed models in production. - Applied some optimisations on specific flows therefore significantly reduced the latency. - Provided numerous hotfixes for critical issues arising between Agile sprints.