Manuel Jesús F.

At Glovo, I played a key role in scaling engineering excellence across the organization. As part of the Fintech cluster—comprising eight engineering teams—I helped define and lead the North Star vision, working closely with tech leads to align engineering direction with product strategy. My role blended high-level technical leadership with hands-on involvement: whether it was supporting teams through critical projects, stepping in during high-pressure situations, or guiding architectural decisions, I thrived on being both a strategic partner and a reliable engineer on the ground.

Beyond my cluster responsibilities, I was an active contributor to the platform organization, particularly enjoying my time working with the Developer Experience team. This cross-cutting collaboration allowed me to drive improvements in internal tooling and engineering workflows, always with the goal of empowering developers to move faster and build better.

Design and development of new features for reviews platform. We work on the whole flow of a review, from ingestion, to processing and analysis until we publish them, at the same time we schedule and deliver corresponding notifications to end users in order to do collection.

Technologies and frameworks

Scala, Java and recently starting with Kotlin as languages. Our platform is based on events so our cornerstone is Kafka, we have streaming services build in Akka, Fs2 and Spring Kafka Integration. In addition we have several APIs build on Finagle/Finch and Spring Rest. We work with different types of datastores depending on their usage, Scylla, Cassandra, MongoDB, ElasticSearch and Redis. Applications are containerized with Docker and deployed in AWS using in house utilities

The main goal of the project is to collect water readings from different manufacturer devices, store them and make some analytical work in real time. The application needs to deal as well with alarms, supply points, workers iteration and so on. There is a whole architecture around data ingestion and processing based on Apache Spark and Spring microservices combination.

Technologies and frameworks:

Java and Scala as main languages, Maven as building tool, Spring Boot as framework for microservices and Apache Spark for big data processing. We include Spring Cloud, Spring Data and Spring Integration as well, as for Apache Spark we use Spark SQL and Spark Streaming. For streaming data between all this components in an asynchronous way we use Apache Kafka, the main gear. Data is persisted in PostgresSQL and Cassandra. Testing technologies include JUnit, Spock, Sprint Test, Wiremock, Mockito, AssertJ and Cucumber. As continuous integration tool we have Jenkins with pipelines. Lastly for our cluster management we have Apache Mesos for deploying our Docker containers.