I build production data platforms and the engineering foundations that reliable analytics, ML, and AI systems depend on.
8+ years across healthcare, fintech, e-commerce, and marketing. Hands-on individual contributor focused on Python, SQL, cloud data platforms, orchestration, data quality, reliability, and production-ready ML/AI infrastructure.
Built and operated Python, SQL, Airflow, and BigQuery pipelines processing billions of records weekly. Owned reconciliation, data-quality controls, backfills, SLA reliability, monitoring, and performance/cost optimization.
Built Snowflake and BigQuery models and pipelines with dbt, Airflow, Python, and SQL for financial, customer, marketing, and ML-facing use cases across Argentina, Mexico, and Colombia.
Redesigned a recurring Ads data workflow at Mercado Libre using Python and SQL, reducing manual processing time by approximately 80% while making the process more repeatable and easier to validate.
These summaries are intentionally sanitized. They describe scope and ownership without exposing employer or client data, source code, schemas, or internal architecture.
Synthetic end-to-end ML system with explicit data contracts, reproducible training, versioned model metadata, batch/online scoring, FastAPI, Docker, tests, CI, and optional MLflow tracking.
Synthetic retrieval platform with validated documents, deterministic indexing, golden-query evaluation, FastAPI search, structured result contracts, an optional MCP tool server, Docker, tests, and CI.
I work best in autonomous engineering roles with clear ownership, strong written communication, and more time building than sitting in meetings.