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

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

I design and deliver production-style data systems that turn raw data into decision-ready products.
My focus is the full lifecycle: ETL/ELT pipelines, data quality contracts, analytics modeling, and stakeholder-facing BI/ML outputs.

I combine Data Engineering (automation, testing, CI/CD, architecture) with Data Analytics (KPI storytelling, dashboarding, business impact analysis) to create solutions that are both technically robust and useful for decision-making.

• Production Data Engineering: I build reproducible ETL/ELT pipelines with Pandera validation gates, automated testing, and CI workflows (133+ tests in production-style projects).
• Database & Analytics Engineering: I design reliable SQL transformation layers (3NF modeling, query tuning, indexing), achieving up to 40% performance improvements in analytical workloads.
• Business Intelligence & Decision Support: I create KPI-driven dashboards and executive-ready narratives, including identification of $16K+ performance gaps for action planning.
• Applied ML for Real Products: I develop explainable predictive systems (e.g., dynamic pricing with 1.2M+ records) and deliver artifacts ready for web/product integration.

• Reduced analytical query runtime by up to 40% through SQL optimization and indexing strategy.
• Identified $16K+ performance/revenue opportunity gaps through BI analysis and KPI storytelling.
• Deployed multiple reproducible data products with 133+ automated tests and CI-backed validation workflows.

RideFare -ETL-Pipeline Project Status

RideFare is currently in active development.

This project is being rebuilt from a notebook-centered ETL demo into a complete, production-style data product for portfolio use.

It combines:

• Data Engineering: reproducible ingestion, validation, transformation, and analytics modeling
• Machine Learning: documented training, evaluation, explainability, and exportable artifacts
• Frontend Product: a polished public web app in Spanish with typed data contracts
• Automation & Delivery: CI workflows, data refresh pipelines, and deployment-ready structure
• Documentation Quality: architecture docs, model documentation, ADRs, and runbooks

      
Actively preparing for C1 certification

End-to-End Data Engineering + ML + Analytics Product

From notebook-based analysis to a reproducible, production-style data product with public delivery.

• Pipeline Modernization: Rebuilt legacy notebook flow into reproducible commands (ridefare ingest, transform, train, export-web) with clear operational interfaces.
• Data Quality by Design: Implemented schema and validation controls with Pandera, stable transformations with DuckDB + dbt, and versioned public artifacts.
• Explainable ML Delivery: Trained and exported XGBoost + SHAP artifacts for transparent model behavior and scenario exploration.
• Public Product Interface: Delivered a Spanish-language Next.js web experience (/dashboard, /como-funciona, /escenarios) powered by deterministic exported JSON.
• Automation & Deployment: Integrated CI validation, artifact refresh workflows, preview/prod deploy pipelines, and release automation.

   

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.

   

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

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

Open to Junior Data Engineer / Data Analyst roles (remote/hybrid, LATAM/US).

I’m ready to contribute from day one in data pipeline automation, analytics engineering, and decision-focused BI.