SQL-first. Cloud-native. Production-minded.
I build reliable data systems with Snowflake, dbt, SQL, Python and AWS, from complex transformation layers to scalable, observable production pipelines.
- 🧱 I build production-grade data platforms, transformation layers, data marts and ELT pipelines.
- ❄️ I work deeply with Snowflake, from modeling and SQL performance to reliability and warehouse optimization.
- 🔄 I use dbt, Airflow and CI/CD to make data workflows testable, repeatable and easier to operate.
- ☁️ I build on AWS, working across data lakes, serverless workflows, orchestration and infrastructure automation.
- 📊 My experience spans media & entertainment, CX/CCaaS, retail and enterprise data platforms.
- 🤖 I integrate AI into engineering workflows where it genuinely improves development, testing or documentation.
Tools change. The responsibilities of a good data platform don't change nearly as fast.
SOURCE INGEST STORE TRANSFORM SERVE
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APIs / DB ──► Pipelines ──► Cloud Data ──► SQL / dbt ──► Analytics
│
┌────────────┴────────────┐
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Reliability Observability
Data Quality Cost
Recovery Performance
I like the messy part of Data Engineering, legacy SQL, growing pipelines, performance bottlenecks, unclear business logic, and systems that need to become reliable enough for production.
| Metric | Engineering Impact |
|---|---|
| 100+ | Production dbt models delivered |
| ~35% | Query latency improvement through optimization |
| ~70% | Manual database reliability checks automated |
| 50+ | Airflow / data workloads supported through CI/CD |
| >99.8% | Release SLA across production workflows |
The interesting part isn't the number itself.
It's figuring out what changed in the system to move it.
For the professional timeline, projects and experience:
📍 Bolivia · Working globally