Leandro Rafael Krüger Gessner

Leandro Rafael Krüger Gessner has worked in the software engineering field since 2019. In 2019, they worked as a Junior Software Engineer at Consistem, where their main activities included legacy code maintenance and refactoring. Technologies used included Caché, HTML, CSS, XML, and SQL. In 2020, they worked at UpFlux Process Mining as a Junior Python Engineer and Integration Developer. As a Junior Python Engineer, their main activities included creation and maintenance of python scripts for data transformation and loading, and technologies used included Python, JSON, Pandas, Apache Spark, Apache Parquet, Excel, and AWS. As an Integration Developer, their main activities included development and maintenance of ETL pipelines and SQL queries for extracting data from clients' databases, transformation and loading of these data, support and resolution of tickets opened by clients, and development of a RPA. Also in 2020, they worked at MEGi9 Soluções as a Junior Java Engineer, where their main activities included legacy code maintenance and refactoring, and technologies used included Java, NXJ, and SQL. In 2022, they worked at Hyperlocal as a Junior Data Engineer, where their main activities included creation and maintaining Python scripts for extracting data and sending it to S3 buckets, creating and configuring database migration tasks, replication instances and endpoints, using AWS DMS (Database Migration Service), and creating and maintaining Glue scripts for transforming and loading of data. Currently, they are working at Franq Open Banking as a Mid-level Data Engineer.

Leandro Rafael Krüger Gessner is currently enrolled in Estácio's Curso Superior de Tecnologia (CST) in the field of Ciência de Dados, which they began in 2020 and is expected to complete in 2022. In addition, they have obtained a number of certifications from Alura, Udemy, HackerRank, and Sololearn since 2021, including Spark: trabalhando com regressão, The Complete Hands-On Introduction to Apache Airflow, Python(Basic), Spark: apresentando a ferramenta, Spark: streaming de dados, Docker: criando e gerenciando containers, Python e TDD: explorando testes unitários, Business Intelligence: trabalhando com Data Warehouse, Amazon Lambda: executando código sem servidor, Amazon S3: Manipule e armazene objetos na nuvem, Scraping com Python: coleta de dados na web, Pandas: formatos diferentes de entrada e saída (IO), SQL (Basic), Data Science: primeiros passos, Python Pandas: tratando e analisando dados, Python Pandas: técnicas avançadas, Python para Data Science: Funções, Pacotes e Pandas básico, Python para Data Science: introdução à linguagem e Numpy, Intermediate Python, and Python Data Structures.