Junior Data Engineer & Data Analyst | Open to Remote/Hybrid (LATAM/US)

I build production-style data systems and decision-focused BI products.

Impact highlights: 133+ tests in CI, up to 40% faster SQL workloads, $16K+ performance gaps identified, 1.2M+ records in ML pricing pipelines.

📍 Guayaquil, Ecuador · 🌎 Open to Remote / Hybrid opportunities

Business problem: Uneven seller performance was reducing total revenue potential.
Analysis performed: Segmentation by seller contribution, variance analysis, and gap quantification using Power BI + DAX.
Impact: Identified a $16.66K performance opportunity and prioritized intervention areas.

Business problem: Marketing needed clearer targeting of high-value customer segments.
Analysis performed: Behavioral profiling by demographics, household structure, tenure, and channel mix.
Impact: Highlighted premium-spend segments and enabled more focused campaign planning.

Business problem: Stakeholders lacked a concise decision view across KPIs.
Analysis performed: Built desktop/mobile KPI dashboards with narrative flow (context → insight → action).
Impact: Reduced reporting friction and improved decision speed for non-technical audiences.

Junior Data Engineer & Data Analyst | Computer Engineering Student (ESPOL, 7th semester)

I build production-style, reproducible data products that combine engineering reliability with business decision impact.
I work across the full cycle: ingestion, transformation, validation, analytics modeling, and stakeholder-facing BI delivery.

• Built ETL/ELT workflows with Pandera validation, automated testing, and CI/CD quality gates.
• Implemented SQL transformation layers and indexing strategies, improving analytical query performance by up to 40%.
• Delivered KPI-driven BI outputs that identified $16.66K performance opportunities for business action.
• Modeled applied ML pipelines (e.g., pricing) over 1.2M+ records with explainability artifacts for transparent decisions.
• Automated reproducible delivery flows from raw data to dashboard/web-ready outputs.

Status: In progress

I am currently refactoring this portfolio repository to present my two professional tracks in a clearer and more strategic way:

• Data Engineer Track: featured projects focused on ETL/ELT, data quality, analytics engineering, reproducible pipelines, testing, and CI/CD.
• Data Analyst / BI Track: featured projects focused on KPI modeling, dashboard storytelling, business insights, and executive reporting.

• Restructuring project sections to make each role path explicit for recruiters and ATS.
• Improving content hierarchy (impact first, stack second, implementation third).
• Standardizing project maturity labels (Production-ready / Active maintenance / Completed).
• Polishing responsive design for mobile and desktop readability.
• Strengthening call-to-action messaging for Junior Data Engineer / Data Analyst opportunities.

Deliver a recruiter-friendly profile that communicates both technical depth and business impact in under 60 seconds.

      
Actively preparing for C1 certification

Production-Style 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.

   

Production-Style 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.

   

Status: Completed | Track: Data Analyst / BI Analyst

• Built a reproducible workflow: raw dataset → Python preprocessing notebook → validated clean CSV → Power BI dashboard.
• Modeled customer behavior across demographics, household composition, tenure, and channels.
• Delivered desktop + mobile report layouts for stakeholder-ready consumption.
• Produced a clear commercial narrative around high-value segments and premium spend behavior.

 

Status: Completed | Track: Data Analyst / BI Analyst

• Identified a $16.66K performance gap across seller performance.
• Surfaced top revenue category with $80.05K for commercial prioritization.
• Analyzed 23 active sellers, with Tulsa highlighted as the strongest market.
• Built with Power BI + DAX + Excel to deliver a concise decision-making dashboard.

Status: Completed | Track: Data Engineer + Data Analyst

• Built a full pipeline: MySQL → Python ETL → validated JSON contracts → web dashboard.
• Integrated Random Forest projections (2026) to combine descriptive and predictive analytics.
• Delivered reliable outputs with 126 automated tests and CI-driven deployment.
• Consolidated ecosystem visibility across teams, players, competitions, and prize performance.

 

Status: Completed | Track: Data Engineer + Data Analyst

• Engineered pipeline: MySQL → Python ETL → JSON outputs → 5-view web dashboard.
• Modeled strategic recovery from -5.58% ROI to +15% target (+20.6 pts).
• Projected +75% productivity uplift with KPI-driven operational analysis.
• Delivered reproducible implementation backed by automated ETL tests.

 

Status: Completed | Track: Data Analyst (Statistical Modeling)

• Validated a Negative Binomial model with goodness-of-fit acceptance (p = 0.6603).
• Processed 309 observations and confirmed mean serve time under 2 seconds (1.945s).
• Automated JSON/PNG exports from R pipeline for dashboard-ready delivery.
• Improved interpretability by packaging statistical outputs into a lightweight web report.

 

Status: Completed | Track: Full-Stack + Applied Analytics

• Built MVP in 48 hours during NASA Space Apps Challenge.
• Processed 10 years of climate-related data for 195+ countries.
• Delivered interactive map workflows with <2s response time for user exploration.
• Recognized as Galactic Problem Solver (Global Nominee).

 

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

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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.