I'm a data engineer. My path here was a bit winding: I studied geology, then made maps for clients around the world as a GIS freelancer.
Along the way I spent a lot of time doing quality control on satellite imagery: reviewing, annotating, and fixing the training data that AI models learn from. After years of cleaning up bad data, one lesson stuck. AI is only as good as the data you feed it. So now I build the pipelines behind it, because somebody has to. 🤷
Six years of Python, based in the Philippines 🇵🇭.
🌋 pylindol: scrapes PHIVOLCS
earthquake data so nobody has to copy it by hand anymore (you're
welcome). It's on PyPI: pip install pylindol
💎 gem-ai: a chatbot that answers your gemstone questions using USGS and Smithsonian documents. It shows its sources, so you don't have to take its word for it.
🧵 okf-weaver: reads your database schema and writes the documentation nobody got around to. (Be honest, it was never getting written otherwise.) A human still reviews it before it ships.
Languages: Python SQL
Google Cloud (I know my way around most of it, but these are the
regulars): Cloud Run Cloud SQL (PostgreSQL, SQL Server) Cloud Storage
Composer Workflows Dataproc
Data warehouse: Snowflake
Infra & CI/CD: Terraform GitHub Actions
Web (not my main thing, but I can find my way around):
HTML CSS JavaScript Next.js
📚 Studying Generative AI and Agentic AI at the Asian Institute of Management (AIM)
🏗️ Building olist-warehouse, a BigQuery warehouse on the Olist Brazilian e-commerce dataset
Happy to chat about data pipelines, messy datasets, or anything that involves turning chaos into clean tables.