Hey there! 👋 Imagine going from a total Excel newbie in 2019 to being the go-to person for data insights, scripting, and dashboard wizardry. That's my story!

Back in the day, my adventure began in an unexpected role: being the bridge between my team and the PMO company on a project that's been my world ever since. Starting with Pipefy, I was initially just trying to make sense of reports and Google Sheets. It was like learning a new language, but guess what? I got hooked on the power of data.

Fast forward a bit, and there I was, venturing into the land of dashboards. Google Looker Studio became my first ally since Power BI and Google Sheets weren’t on speaking terms yet. Crafting those initial dashboards was no walk in the park, especially in a company where data wasn't the main actor on stage. Yet, I was determined to spotlight how crucial data could be for our decisions. Today, those dashboards aren’t just tools; they’re essentials, relied upon by top brass and key stakeholders alike in the projects I’m passionate about.

Emboldened by the success of my dashboard crusades, I introduced Power BI to our toolkit, marking a new chapter in our data-driven narrative. And because I can't help but dive deeper, I'm now expanding my arsenal with Python, SQL, and more. It's like I've found the secret passage to the treasure trove of data analytics, and there's no turning back.

So, what's in my utility belt? 🛠️

• Data Storytelling: From untangling the mysteries of Pipefy reports to mastering the art of dashboard creation, I turn data into compelling narratives that drive action.

• Dashboard Alchemy: I transform raw data into gold—visual, impactful, and indispensable tools for decision-making. My dashboards don't just exist; they make a difference.

• Trailblazing: In a realm where data wasn’t king, I championed its cause, integrating Power BI and pioneering analytics practices that have become foundational to our business strategy.

• Continuous Learner and Innovator: Every day is a quest for knowledge, whether it's mastering Python, or unraveling the secrets of SQL. My journey is about pushing boundaries and elevating our data game.

From a novice in the art of Excel to a maestro in the symphony of data analytics, my journey is a testament to the power of curiosity, determination, and the transformative potential of data. Ready to join me on this adventure?

For more details, check out my CV.

Online sales dashboard, created as a final project for the Data Analyst Professional course.

• Company overview between 2017 and 2019
• In depth analysis of revenue, profit, margin, invoices and customer coverage (YoY, MoM, YTD)
• Continental goals analyzed and apportioned
• Drill-through for main brands and products

• ETL using SSMS and basic SQL language
• Advanced data modelling and DAX using calculation groups and slicers
• Navigation buttons
• DataViz with Figma and advanced storytelling

Human resources dashboard of a logistics company (Klog), created during the PBI Week event.

• Comprehensive overview of hiring and terminations between 2002 and 2020
• Short-term termination rate analysis (if no more than 60 days passed between hiring and termination), requested by the HR manager
• Year-by-year updated turnover analysis

• ETL on Excel spreadsheets
• Simple data modelling and DAX
• Slicers and parameters within visuals for multiple combinations
• DataViz with Figma

Taking a closer look at the turnover rate, we realize that age group doesn't significantly impact this KPI, as it doesn't vary much from one age group to another. However, if we examine by position, it becomes apparent that assistants have a hiring issue that deserves attention: not only is their turnover higher, but they also represent a larger portion of the rate of bad hires (we have a lot of turnover because a greater number of people resign within less than 60 days). A good insight in this regard might be to implement a more rigorous selection process for this position.

Sales dashboard for PBIDist Company, created during the PBI Week event.

• Brief company overview during 2019 and 2020
• Treemap for in depth analysis of company revenue
• Top influencers chart

• ETL on Excel spreadsheets grouped by folder
• Simple data modelling and DAX
• Tooltip with URL images
• DataViz with Figma

Overall, by examining the revenue, gross margin, and invoices issued indicators, we observed a considerable increase in the number of invoices from one year to the next. This trend suggests a reduction in the average ticket size (there were more transactions, but with smaller quantities per sale, resulting in a lower value per invoice issued). Ultimately, revenue went up, indicating that this strategy might have worked.

It's also worth noting that wheat flour was sold much more than other products individually, as there was a 224% increase in the issuance of invoices compared to last year.

Another point of analysis is the month of October, where a significant decline was noted. While this drop didn’t appear significant when looking at the sales team’s performance, a closer examination of wheat flour sales revealed a notable disparity in revenue between 2020 and 2019.

Financial dashboard for a car dealership (Xperia Automotive), created during the PBI Week challenge.

• Brief financial overview of the company between 2017 and 2018
• Year-over-Year (YoY) analyses
• Analyses of the different types of inflow and outflow

• ETL on Excel spreadsheets grouped by folder
• Simple data modelling and DAX
• New chart bar visualization (feb/24)
• DataViz with Figma

Page 2, which details the analysis in the main states that generated revenue, is still under construction, but it is already clear that the state of São Paulo (SP) accounts for more than 90% of the overall revenue that entered the company.

Furthermore, other possible insights include new states that have started to contribute to the revenue in 2018: Rio Grande do Norte (RN), Tocantins (TO), and Acre (AC).

Sales dashboard, created as a final project for the Power BI Fundamentals course.

• Company overview between 2017 and 2019
• Managerial analysis and team in-depth analysis
• Product analysis and seasonality

• ETL on Excel spreadsheets
• Basic data modelling and DAX
• Introduction to forescasting
• DataViz with basic Figma

Employing the principles of Monte Carlo simulation, this Python-based project investigates the impact of introducing an agent of chaos—a gym-goer who consistently returns dumbbells to incorrect locations—within a community of diligent members who strive to maintain order.

Imagine a gym, a temple of health where the sanctity of order reigns supreme. Dumbbells are lifted and returned with almost religious precision. But what if one individual decides to defy this sacred rule? "Chaos in the Gym" simulates this scenario, employing the Monte Carlo method to inject a degree of unpredictability into an otherwise harmonious environment.

The program models a virtual gym where one "agent of chaos" operates amidst 10, 20, or more participants who adhere to the rules. This simulation iteratively runs scenarios with varying numbers of rule-abiding gym-goers to quantitatively measure the impact of disorder. Through this Monte Carlo approach, the project not only visualizes the immediate effects of misplaced dumbbells but also forecasts the broader implications of such chaos over time.

• Programming Language: Python

• Libraries:

  • Random: For generating random choices, simulating the unpredictable behaviors of gym users.
  • Seaborn: For data visualization, specifically for plotting the distribution of chaos levels in the gym.

• Development Tools:

  • VSCode: As the primary code editor, offering a robust environment for Python development. Check out the full code here.
  • Jupyter Lab: Used for interactive code testing and visualization, allowing for a more iterative approach to development and analysis.

A simple rock, paper, scissors game for Mac Terminal.

The goal of this project was to develop a simple game, using basic Python structures.

• Basic Python language
• Main code developed on VSCode to play on Mac Terminal and final file with markdown explanation on Jupyter Lab

A basic management software created for a car rental company, created as one of the projects in the Python Starter couse.

The goal of this project is to develop an algorithm for a car rental company. The customer should be presented with car options and prices. After selecting the car and duration of rental, there's a grand total at the end.

• Basic Python language
• Developed on Jupyter Lab and VSCode

• LinkedIn
• E-mail
• Mobile