Henrique Leite
About Henrique Leite
Henrique Leite is a Senior Data Engineer at BairesDev, where he has worked since 2021, focusing on modernizing data infrastructure for tech companies. He has a background in software and data engineering, with experience in ETL processes, cloud-based data pipelines, and machine learning integration.
Work at BairesDev
Henrique Leite has been employed at BairesDev as a Senior Data Engineer since 2021. In this role, he has contributed to the modernization of data infrastructure for a U.S.-based tech company. He led the architecture and development of scalable, cloud-based data pipelines, enhancing the company's data processing capabilities. His work supports the strategic objectives of the organization by ensuring efficient data management and accessibility.
Previous Experience in Data Engineering
Before joining BairesDev, Henrique Leite worked as a Data Engineer at Banco Itaú from 2018 to 2021. His responsibilities included enhancing data processing capabilities and integrating machine learning models into data pipelines. Prior to that, he served as a Software Engineer and Data Engineer at Banese from 2016 to 2018, where he designed and implemented ETL processes using Python and SQL, and utilized Docker and Kubernetes for deploying data services.
Education and Expertise
Henrique Leite studied Computer Engineering at Universidade Federal de Sergipe from 2015 to 2021. His education provided him with a strong foundation in data engineering principles and practices. He has expertise in big data technologies, including Apache Hadoop and Kafka, and has experience in utilizing machine learning frameworks such as TensorFlow and scikit-learn for predictive analytics.
Technical Skills and Tools
Henrique Leite possesses a range of technical skills relevant to data engineering. He has experience in designing ETL processes and ensuring data quality through rigorous testing and validation. His proficiency in Python and SQL supports his work in data manipulation and analysis. Additionally, he has utilized Docker and Kubernetes to enhance the deployment and scalability of data services, contributing to the overall efficiency of data operations.