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

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

I am an engineer focused on the complete data lifecycle: from designing ETL/ELT pipelines and data quality frameworks to performing deep exploratory data analysis (EDA) and deploying Machine Learning models in production-ready environments.

My approach combines data engineering (pipeline automation, query optimization, CI/CD) with business intelligence (stakeholder reporting, KPI dashboards). This allows me to not only build robust data infrastructure but also extract actionable insights that drive data-driven decisions.

• Data Pipeline Automation: End-to-end architectures with strict data validation gates (Pandera) and 133+ automated tests.
• Database Engineering: Implementing 3NF design and query optimization, achieving 40% performance boosts via indexing strategies.
• Machine Learning: Building predictive models for dynamic pricing on real-world datasets of 1.2M+ records.
• Business Intelligence: Uncovering $16K+ financial gaps and delivering clear, interactive reporting for stakeholders.

      
Actively preparing for C1 certification

End-to-End Multi-Source Data Engineering Platform

Tracking real-time developer technology trends by orchestrating data from GitHub, StackOverflow, and Reddit into a unified analytics engine.

• 🌐 Multi-Source ETL: Consolidates developer signals from GitHub, StackOverflow, and Reddit into a canonical pipeline.
• 🛡️ Data Quality Gates: Enforces schema and validation rules with Pandera data contracts.
• ⚡ Modern Analytics Engine: Uses DuckDB for trend computation, ranking, and lightweight analytical workloads.
• ✅ Production Discipline: 133+ passing tests with automated CI/CD workflows and scheduled refreshes.
• 📱 Delivery Layer: Serves insights to a Flutter Web dashboard with stable bridge outputs for frontend consumption.

 

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.

 

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
💻 Languages
⚙️ Data Engineering & DBs
🤖 Machine Learning
🧪 Testing & Quality
📊 Visualization & BI
🌐 Web & Mobile
🚀 DevOps & Cloud
📚 Learning

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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.