Junior Data Engineer & Data Analyst | Building Reliable Pipelines + BI-Ready Data Products

I turn messy, multi-source data into validated pipelines, analytics-ready models, and decision-focused dashboards.

Proof of impact: 133+ automated tests · 1.2M+ records processed · up to 40% faster SQL workloads · $16.66K opportunity identified

📍 Guayaquil, Ecuador · Open to Trainee / Junior Data Engineer and Data Analyst / BI Analyst roles · Remote / Hybrid LATAM-US

Automated audit of flagship projects (CI/CD, tests, docs, demos, and latest updates).

Self-directed Data Engineering · ML · Analytics Product

End-to-end pipeline modernization with reproducible commands, validation, ML artifacts and public delivery.

• Problem: Ride pricing analysis started as a notebook-style workflow with limited reproducibility and weak delivery structure.
• Built: CLI-based pipeline stages for ingestion, transformation, training and web export (ridefare ingest, transform, train, export-web).
• Engineering work: Pandera validation, DuckDB/dbt transformations, XGBoost + SHAP explainability, CI checks, deterministic JSON exports, preview/prod deploy pipelines and release automation.
• Impact: Production-style pricing intelligence platform processing 1.2M+ records with public demo routes and transparent ML outputs.
• Stack: Python, Pandera, DuckDB, dbt, XGBoost, SHAP, GitHub Actions, Next.js.

   

Self-directed Data Engineering · Analytics Engineering

Multi-source ETL platform with data contracts, CI/CD, DuckDB analytics and dashboard delivery.

• Problem: Developer trend signals are fragmented across GitHub, StackOverflow and Reddit.
• Built: Unified analytics platform for ingesting, validating, transforming and exposing trend metrics.
• Engineering work: Python ETL, Pandera data contracts, DuckDB analytical transformations, CI/CD validation, scheduled refresh workflows and frontend-ready outputs.
• Impact: 133+ passing tests, automated quality gates and public dashboard for technology ranking and monitoring.
• Stack: Python, Pandera, DuckDB, GitHub Actions, Flutter Web, APIs.

   

Bootcamp-backed BI Case · Independently polished for portfolio delivery

Customer segmentation, KPI storytelling and stakeholder-ready Power BI reporting.

• Problem: Business stakeholders needed a clearer view of customer value, premium spending behavior and segment-level opportunities.
• Built: Reproducible workflow from raw dataset to Python preprocessing, clean CSV and Power BI dashboard.
• Analytics work: DAX measures, customer segmentation, desktop/mobile layouts and executive narrative from context to insight to action.
• Impact: Decision-focused dashboard supporting campaign planning through clear KPI storytelling and premium-spend segment discovery.
• Stack: Power BI, DAX, Python, data cleaning, KPI modeling, dashboard storytelling.

 

BI / Revenue Opportunity Analysis

Commercial KPI analysis with measurable revenue gap identification.

• Problem: Seller performance varied significantly across markets and categories, reducing total revenue potential.
• Built: Power BI dashboard with seller segmentation, revenue comparison, category analysis and market-level performance views.
• Analytics work: DAX measures, KPI modeling, seller contribution analysis, variance analysis and commercial prioritization.
• Impact: Identified a $16.66K seller performance gap, surfaced a top revenue category worth $80.05K, analyzed 23 active sellers and highlighted Tulsa as the strongest market.
• Stack: Power BI, DAX, Excel, KPI analysis, revenue analytics.

Hybrid Data Engineering + Analytics delivery for the LATAM eSports ecosystem.

• 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 visibility across teams, players, competitions, and prize performance.

 

Operational analytics platform combining ETL outputs, KPI views and strategic recovery modeling.

• 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.

 

Statistical modeling case packaged into dashboard-ready JSON/PNG outputs and a lightweight web report.

• 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.

 

48-hour full-stack + applied analytics MVP recognized as a NASA Space Apps Global Nominee.

• 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

      
Actively preparing for C1 certification

Junior Data Engineer & Data Analyst | Computer Engineering Student (ESPOL, 8th semester)

I’m a Computer Engineering student at ESPOL, currently in my 8th semester, building a dual-track data career across Data Engineering and Business Intelligence. My strongest focus is designing reliable, reproducible data pipelines with validation, testing and automated delivery, while also translating analytical outputs into dashboards and business recommendations.

• Self-directed Data Engineering projects with ETL/ELT workflows, Pandera validation, automated testing and CI/CD quality gates.
• Analytics engineering foundations with SQL, DuckDB/dbt concepts, transformation layers and query optimization.
• BI/Data Analysis delivery through Power BI, DAX, KPI modeling, customer segmentation and revenue opportunity analysis.
• Portfolio-grade data products that connect raw data, validated artifacts, dashboards and clear stakeholder narratives.

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.