João L.
Data Engineer

Skills

Python

C++

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Bio

Specializing in data analysis and business intelligence within strategic and operational contexts, leveraging expertise to generate valuable insights from processes through data. Currently transitioning to a career in Data Engineering, with a focus on identifying business requirements, collecting raw data, and developing data pipelines to enhance data value. Proficient in the extraction, transformation, and storage of data from diverse sources for comprehensive analysis and insights. Demonstrates strong collaborative skills, working effectively with multidisciplinary teams to identify and disseminate data patterns. Possesses over three years of experience as an Electrical Engineer in the electrical energy sector, concentrating on business intelligence, software development, and data analysis.

Data Analyst
11/1/2019 - 5/1/2020

Led the implementation of an AWS cloud BI platform project, focusing on data collection and preparation within AWS Datalake in collaboration with the development team. Utilized Python, particularly leveraging AWS Glue, for data cleaning operations to ensure data quality and consistency. Created insightful data visualizations using AWS QuickSight to enable data-driven decision-making. Developed expertise in Python and Pandas, and demonstrated proficiency in various AWS services such as Datalake, Glue, and QuickSight.
Electrical Engineer
12/2/2018 - 7/2/2019

Worked on the development and maintenance of the 'SINAPGrid' electrical network analysis software using C++. Performed extensive analysis of occurrences in electric distribution networks utilizing Python. Provided thorough documentation of software source code, ensuring clarity and ease of maintenance. Conducted bug fixes and managed the setup of new software versions, contributing to the overall stability and reliability of the system.
Middle office
7/2/2017 - 1/1/2018

Automated and developed BI processes in energy using Python, MySQL, and VBA, which facilitated the creation of dashboards on the internal intranet. Extracted and evaluated meteorological variables to develop new pricing models, enhancing predictive accuracy. Prepared weekly studies of energy price scenarios using the Decomp model, contributing to strategic decision-making. Supported energy trading activities, focusing on the spot market (CCEE), and provided reliable pricing insights.
Internship
7/2/2015 - 6/2/2017

Optimized payment processes for electric transmission companies through web scraping techniques, achieving an 80% reduction in execution time and efficiency using Python. Prepared analytical reports integrating data from energy price models, plant flow forecasts, energy generation statistics, and meteorological data. Supported in projecting calculations of transmission tariffs for distribution and generation companies (TUST/TUSDg). Monitored projects focused on enhancing energy balance efficiency.