I don't just analyze data. I build the systems that make analysis possible.

Junior Data Engineer & Analyst | 7th Semester ESPOL, Ecuador

I am an engineer focused on the complete data lifecycle: from building robust architectures (ETL/SQL) to analyzing trends and deploying Machine Learning models.

Unlike a traditional analyst, my technical background allows me to not only visualize data but also build and optimize the systems behind it. My goal is to transform complex raw data into clear, actionable strategies that drive business growth.

• Data Engineering: Automating ETL pipelines and optimizing database queries (40% performance boost).
• Machine Learning: Building predictive models for dynamic pricing and real-world scenarios.
• Business Intelligence: Identifying financial gaps ($16k+) and visualizing KPIs for decision-making.

End-to-End Data Engineering & Machine Learning Project

Simulating price optimization for ride-hailing apps using a data architecture with 1.2 Million records.

• 🔧 ETL Architecture: Engineered an automated Python pipeline to ingest 1.2M+ raw records, using complex SQL JOINs to clean and consolidate a final dataset of ~600k verified trips in SQLite.
• 🤖 Machine Learning: Trained a Random Forest Regressor to predict dynamic pricing (Baseline RMSE: $9.00).
• 📊 Key Insight: Feature importance analysis revealed distance (>0.6) and surge_multiplier as the absolute dominant factors, proving granular weather data added unnecessary noise.
• Tech Stack: Python, SQL, Pandas, Scikit-Learn, Plotly.

Award: Galactic Problem Solver (Global Nominee)

• Innovation: Built a full-stack web app analyzing 10 years of NASA satellite data across 195+ countries with <2s response time on interactive maps.
• Impact: Developed MVP in a 48-hour hackathon, integrating real-time APIs to predict global extreme weather probabilities.
• Tech: Python (Flask), React, TypeScript, Leaflet, Plotly.

 

End-to-end Data Engineering for Agriculture

• Result: Engineered a Python ETL pipeline (covered by 14 unit tests) that modeled a strategic turnaround, projecting an ROI improvement from -5.58% to +15% (+20.6 pts) and a +75% boost in productivity.
• Architecture: Built a robust MySQL -> Python -> JSON pipeline feeding a 5-page interactive dashboard for operational tracking.
• Tech: MySQL, Python, Pandas, Pytest, JS/Bootstrap.

 

SQL Database Design & Query Optimization

• Achievement: Optimized a 3NF MySQL database with composite indexes (idx_competencias_tipo_compid), reducing execution time by 40% for complex multi-table queries.
• Scope: Processed historical performance data for 15 teams across 8 LATAM countries managing a $325,000 total prize pool.
• Tech: MySQL 8.0, Advanced SQL (CTEs, Window Functions), Vanilla JS, Chart.js.

 

Business Intelligence

• Insight: Analyzed sales distribution across 23 active sellers ($28.4K avg), uncovering a critical $16.66K performance gap between top and bottom performers.
• Impact: Identified "Meat" as the top revenue driver ($80.05K) and Tulsa as the premier market (20 top clients), delivering actionable KPIs for data-driven decisions.
• Tech: Power BI, DAX, Excel.

Scientific Research & Data Modeling

• Validation: Built an automated R pipeline to validate a Negative Binomial Distribution model (k=3, p=0.3) on 309 observations, achieving a statistically significant p-value of 0.660.
• Impact: Tracked a mean serve time of 1.945s (<2s threshold) and exported JSON/PNG assets into a dynamic JS web dashboard.
• Tech: R (Tidyverse, ggplot2), HTML/CSS/JS.

 

Category	Technologies
🔧 Data Engineering & Analysis
🤖 Machine Learning
📊 Visualization & BI
🌐 Web & App
☁️ Cloud & DevOps

Real-time stats powered by WakaTime — tracking every line of code I write.

**I'm a Night 🦉**

🌞 Morning                0 commits           ░░░░░░░░░░░░░░░░░░░░░░░░░   00.00 % 
🌆 Daytime                255 commits         █████████░░░░░░░░░░░░░░░░   35.56 % 
🌃 Evening                369 commits         █████████████░░░░░░░░░░░░   51.46 % 
🌙 Night                  93 commits          ███░░░░░░░░░░░░░░░░░░░░░░   12.97 % 

📅 I'm Most Productive on Saturday

Monday                   72 commits          ███░░░░░░░░░░░░░░░░░░░░░░   10.04 % 
Tuesday                  89 commits          ███░░░░░░░░░░░░░░░░░░░░░░   12.41 % 
Wednesday                140 commits         █████░░░░░░░░░░░░░░░░░░░░   19.53 % 
Thursday                 120 commits         ████░░░░░░░░░░░░░░░░░░░░░   16.74 % 
Friday                   28 commits          █░░░░░░░░░░░░░░░░░░░░░░░░   03.91 % 
Saturday                 186 commits         ██████░░░░░░░░░░░░░░░░░░░   25.94 % 
Sunday                   82 commits          ███░░░░░░░░░░░░░░░░░░░░░░   11.44 % 

📊 This Week I Spent My Time On

💬 Programming Languages: 
Markdown                 57 mins             ██████████████████░░░░░░░   71.85 % 
Dart                     18 mins             ██████░░░░░░░░░░░░░░░░░░░   22.84 % 
Python                   4 mins              █░░░░░░░░░░░░░░░░░░░░░░░░   05.31 % 

🐱‍💻 Projects: 
Technology-trend-analysis1 hr 20 mins        █████████████████████████   100.00 % 

Last Updated on 05/03/2026 01:04:09 UTC

🤝 Open to Opportunities

I'm a 7th-semester Computer Engineering student at ESPOL actively looking for Junior Data Engineer or Data Analyst roles where I can contribute from day one.