Felipe Jacheto

Scaling Power BI usage throughout the organization is not a straightforward task. The natural tendency is for dozens of individual developers, with or without data knowledge, to build Power BI on their own, with no documentation or quality standards. In an effort to organize the Power BI's in the organization, I created a dashboard that monitors other published reports.

The dashboard has several key components, including Last refreshes, which shows the last N refreshes of every published Power BI, making it extremely useful for spotting errors. Data Sources, which shows all data sources automatically from API. Excel Documentation, which is a single documentation Excel that every developer needs to fill when publishing new reports. This simplifies life because people usually do not take time for documentation, so reducing that to a 3-minute process increases overall adoption. Additionally, Usage Metrics are included, which function similarly to Power BI's default usage metrics, showing how many views and viewers each page from each report had over the last weeks.

I created a one-stop-shop Power BI for the Finance department using SAP data, which required solving several layers of complexity. I had to ensure data quality and consistency across different departments and cost categories for accurate reporting, which demanded constant communication and alignment with the business. Additionally, I optimized the data model to improve Power BI performance, requiring a high level of understanding of how Power BI calculations work. The dashboard needed to be visually appealing, easy to navigate, and allow users to quickly access relevant information, requiring deep design knowledge to facilitate user instant comprehension of the visuals and how they relate to each other. As users utilized the dashboard, there was a constant flow of improvement suggestions coming from multiple people, and being able to manage what to implement in a way that works for everyone was crucial to adapt to evolving business needs.

Our company implemented a Business Intelligence infrastructure from scratch, using mainly Azure resources and tools. The project started in 2018. I joined at the end of 2019.

In 2019, I acted as a data engineer, creating data pipelines for the financial department. In 2020 I became the technical support of the team. I made sure everything in production worked without errors and generated value that satisfied clients. At the time, the project had two pillars: Data and RPA infrastructure. In 2021, I switched to the front line, working as a data engineer (creating data pipelines and integrations with the company's systems), and a DevOps engineer (constantly evolving the architecture with improvements in the infrastructure and governance).

At Visagio, there are several groups of people who gather together in Learning Organizations (LO) to leverage the knowledge and actuation of certain topics or industries. For example, there is the Supply Chain LO, Digital Transformation LO, Financial LO, RPA LO, Analytics LO, etc.

I participate in the Data and BI LO, leveraging data governance (and all other data management siloes). I spend a good portion of time studying data governance, reading books and online articles, and creating knowledge materials (PowerPoint presentations describing what it is and its importance, then entering into the details of each silo). I secured the DAMA certification as a data management certified professional. I created materials to spread the knowledge of data governance across the company, presented classes and meetups about the topic, and delivered a presentation outlining proposals on selling new projects with data governance-related capabilities

I spent a year in an open scope project automating repetitive work, improving the business performance with technology. I completed various tasks, with my primary role being the creation of robots and automating processes.

This was my final project at University. It is a 1.5 meter (five feet) service robot that can map the environment and walk in a room, avoiding walls, obstacles, and humans. I developed it with three others. I created the software, the coding, and intelligence portions of the robot. We used ROS Kinetic running in Ubuntu for the robot's brain.

Our initial purpose was to create about 30 software bots to automate several stages of various processes. We didn't have robust RPA tools such as UiPath, and the client did not intend to buy any sophisticated tools either. However, we realized that maintenance wouldn't be viable if there weren't a system to manage the activity workflow, robot parameters, monitoring executions, and schedules. We created our own "robot and analyst workflow management system" to solve that.

The system was developed in C# to orchestrate software bots on several computers. It consisted of a controller, a database, an executor, software robots, and a UI.

This project was made for a two-semester Python course at university. It was my first time using Python, but I got so involved that I created something beyond expectations. The game uses the Pygame library as the render engine and a physics engine made from scratch to simulate gravity and different object interations (this was the most challenging part by far, but well worth learning).

This was a control project I did at my university. It is a system with a resistive plate and two servos to control the X and Y inclination of the plane. The goal is to maintain the ball in a chosen position on the plane.