Leandro R.
Data Engineer

Skills

Github

Microsoft Sql Server

Oracle Database

Java

Python

Postgresql

Amazon Aws

Google Cloud

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Bio

A Data Engineer with a foundational background in technology and experience in software development. Demonstrates proficient knowledge in Python and expertise in ETL tools such as Apache NiFi, Airflow, MageAI, and Pentaho Data Integration. Holds substantial experience with leading cloud services, including AWS, Azure, and GCP. Areas of interest predominantly focus on data and backend development.

Mid-level Data Engineer
1/1/2023 - 8/1/2023

Developed expertise in maintaining and creating data pipelines using AirFlow and Apache NiFi. Successfully constructed and maintained a Data Lake and Data Warehouse on Google Cloud, demonstrating proficiency in both tools and frameworks essential for robust data management solutions.
Junior Data Engineer
8/1/2022 - 10/1/2022

Specialized in data collection from diverse sources, leveraging advanced cloud technologies in combination with Python. Utilized AWS Lambda for scalable and efficient serverless computing to streamline the process.
Junior Python Engineer (data transformation)
4/1/2022 - 6/1/2022

Developed and maintained Python scripts for efficient data ingestion and transmission to AWS. Acquired advanced skills in Python programming, focusing on automation and data pipeline creation. Gained expertise in utilizing AWS services, such as S3 and Lambda, to streamline data workflows and ensure secure and reliable cloud storage and processing. Enhanced abilities in debugging, optimizing code performance, and implementing best practices for data handling and AWS integration. Fostered a detailed understanding of cloud-based data solutions and their applications in various technical environments.
Junior Integrations Developer
12/1/2020 - 3/1/2022

Led the creation and maintenance of robust data pipelines, demonstrating advanced proficiency in Python, SQL, and Spark. Utilized tools and frameworks such as Apache Airflow for task scheduling and data workflow automation. Ensured data quality and consistency by implementing rigorous validation checks and error-handling mechanisms. Leveraged cloud platforms like AWS and Google Cloud for scalable data storage and processing. Employed Docker for containerization to streamline the deployment of data pipeline components. Engaged in continuous integration and continuous deployment (CI/CD) practices using Jenkins, enhancing the efficiency and reliability of data operations. Cultivated strong skills in troubleshooting and optimizing data pipelines, reducing processing time and improving overall system performance.