About the company
tech lead who can analyze and design microservices solutions (rest and event-based) for application integration using the new interoperability layer and who can technically guide the teams that will develop these components, ensuring that the implementation is carried out according to established standards and guidelines.
application analysis: interoperability design: define integration patterns to allow disparate systems (legacy systems, rest apis, databases) to publish or consume data in kafka.data management: design of the data model, as well as the data transformation and mapping strategy between systems, ensuring semantic interoperability.implement applications (apis, microservices) that use java apis or compatible languages to write and read data from topics.real-time etl implementation: configure data processing pipelines using kafka connect to continuously move information between systems and platforms such as s3, mongodb, elasticsearch, etc.gestión del ecosistema kafka: deploy and maintain kafka clusters, managing brokers, partitions, and replicas to ensure high availability and fault tolerance.data transformation: use kafka streams to process, clean, and transform messages as they occur.knowledge in security protocols, encryption, and regulatory compliance, data protection.understanding of integration platforms (esb), data repositories, etc.engagement with business, architecture, legacy systems, and front-end to define services.design of component, context, sequence, activity, deployment, and high-level database entity-relationship diagrams.development in: rest and event microservices, java j2ee, spring, spring boot, hibernate, mongodb, mongodb, javascript/typescript.application servers: tomcat, weblogic, openshift.
apache kafka core: deep knowledge of kafka architecture (brokers, topics, partitions, offsets, producers/consumers api).kafka connect: experience in code-free data integration using predefined connectors (jdbc, mongodb, s3).kafka streams/ksqldb: capabilities for real-time data stream manipulation and transformation.message structure: use of efficient serialization formats such as avro, protobuf, or json.programming languages: java or scala (mainly), supplemented with python, go, or .net.infrastructure: experience in cloud environments (aws, confluent cloud) and orchestrators such as kubernetes (openshift).apache kafka, jenkins, sonarqube, maven, git, jira, intellij, eclipse, uml, apache camel, oracle.