Carlos Henrique Jacinto

Atividades

• 06/2021 - 11/2024

  Project (PROADI-SUS DIANA) in partnership with the Ministry of Health and the unified health system in Brazil (SUS) to assist in demands and provide architecture, data governance and automations in the datalake: - Development of automated and managed DAG's; Implementing lakehouse features (Delta Lake); Management of automated processing, tasks and pipelines using Airflow; Implementing serverless to lower the processing cost; Using and managing cloud services (AWS); Using of different data formats: txt, csv, json, parquet, orc; Processing of large masses of data using Hadoop, Spark and Apache technologies (Hive, Sqoop and others); Implementing ETL and EL on structured and semi-structured data; Best practices, data security, standardization of tasks and pipelines; Data Analysis, Data segregation (Raw, Stage, Analytics) and Medallion architecture (Bronze, Silver, Gold); Implantation of data metrics, dashboard, reports, logs and monitoring - CI/CD with Gitlab, Azure Devops, AWS; Data cataloging and data governance; Constantly studying new technologies, making PoCs, to improve Datalake ecosystem.

• 06/2021 - 11/2023

  Project (PROADI-SUS DIANA) in partnership with the Ministry of Health and the unified health system in Brazil (SUS) to provide support and improvements in the architecture of the microservices of the National Health Data Network (RNDS): - Iac with Terraform; Automation using Kubernetes; Using and managing cloud services (AWS); Constantly studying new technologies, making PoCs, to improve systems architecture; Best practices, standardizations for API development - Access control, analytics, metrics, transaction limit, developer portal; Management of APIs using WSO2, Kong and AWS API Manager; Dashboard, reports and logs Using Docker, Kubernetes, Terraform, Azure DevOps, AWS, RabbitMQ, WSO2, Keycloak, Kong API, Gitlab, Prometheus and Grafana.

• 01/2020 - 06/2021

  Project (PROADI-SUS MARIA) in partnership with the Ministry of Health and the unified health system in Brazil (SUS) to develop a collaborative platform for research and analysis of medical images. “Plataforma Nacional de Armazenamento e Inteligência Artificial em Imagens Médicas para Pesquisa, Inovação e Suporte à Decisão Clínica”. - Development of a web platform for applying Machine Learning models in the Healthcare area. - Creation of pipelines for executing Machine Learning models. Using: - Microservices Architecture; GoLang, Python, Swagger - MongoDB, PostgreSQL - RabbitMQ, ElasticSearch, KeyCloak; Docker, Kubernetes, Helm; AWS, AzureDevops, CI/CD - Datadog, Kibana.