I’m a Cloud Architect and Data Engineer focused on designing scalable, secure and cost-efficient data platforms in the cloud.
My work lives at the intersection of data, cloud infrastructure and AI, helping companies move from spreadsheets and ad-hoc scripts to robust, production-grade analytics ecosystems.

I specialize in building end-to-end data pipelines and analytics architectures on AWS, Oracle Cloud, Azure and Google Cloud, enabling use cases such as BI dashboards, predictive models, financial analytics and real-time monitoring.

Beyond the technical side, I enjoy translating complex systems into clear, actionable insights for stakeholders — from engineers to executives.

🧠 Core Focus:

• Designing cloud architectures for analytics, BI and AI workloads (AWS / OCI / Azure / GCP)
• Building ETL/ELT data pipelines with SQL, Python and modern data tooling
• Implementing data models, data lakes and data warehouses for decision-making
• Applying machine learning and time series analysis to financial & operational data

💎 “Good cloud architectures turn data chaos into predictable, observable systems you can actually trust.”

Financial Analytics Dashboard hosted on AWS · Static site + Cloud Architecture

• Static frontend (HTML/CSS/JS) deployed on Amazon S3
• Global content delivery with Amazon CloudFront
• Secure, cost-efficient setup using S3 bucket policies, encryption at rest and versioning
• Designed as a portfolio blueprint for small-business financial analytics in the cloud

Modular Data Platform for BI, reporting and advanced analytics

• Data ingestion from CSV/Excel, APIs and databases into data lake (S3 / OCI Object Storage)
• Transformation layer with Python + SQL and orchestration (Airflow / native cloud services)
• Dimensional modeling for data warehouse use cases (star schemas, facts & dimensions)
• Ready to plug into Tableau / Oracle Analytics / QuickSight for executive dashboards

Budget & Compliance Dashboards for government-style environments

• Modeled financial and accounting data following harmonization and compliance frameworks
• Interactive dashboards for KPIs, trends, drill-downs and variance analysis
• Built on Oracle Analytics Cloud (OAC) with data sourced from cloud databases
• Focus on traceability, governance and auditability of financial indicators

Real-Time Market Data Pipeline as a bridge between quant and data engineering

• Ingestion of crypto market data from exchanges into a streaming layer
• Storage in cloud data lake + warehouse for historical analysis
• Feature engineering for time-series ML models and monitoring of trading signals
• Focus on observability (logs, metrics, dashboards) and cost control in the cloud

🎓 MSc in Economic and Financial Engineering — Universidad La Salle (In Progress)
🎓 AI Full Stack Bootcamp — KeepCoding (Completed)
🎓 Big Data · ML · DS Program — KeepCoding (Completed)
🎓 Data Science Program — Colegio Bourbaki (Completed)
💻 Full Stack Developer Program — U-Camp (Completed)

🧩 “In cloud and data engineering, boring architectures beat exciting outages every single time.” — Fernando Cuéllar