I build data-driven back-ends that turn raw events into insight and revenue. Trained as a Computer Science cum laude grad from UPLB and tested in fast-moving SaaS teams, I architect Python services on Google Cloud Functions and AWS Lambda, model analytics warehouses in BigQuery, and expose clean REST/GraphQL APIs backed by MongoDB or PostgreSQL. In my most recent project I replaced a manual Shopify reporting workflow with a serverless ingestion pipeline (Cloud Functions ? dbt ? BigQuery) that fed Looker Studio dashboards and slashed reconciliation time from three hours to ten minutes. At Shopee I optimized terabyte-scale sentiment ETL with Python UDFs and partitioned tables, cutting query costs by 25%; at GCash I automated KPI reporting by streaming Jira data into a BigQuery mart on an hourly schedule. I treat infrastructure as code (Docker + GitHub Actions), enforce data quality with Great Expectations, and write thorough unit and integration tests so releases ship confidently. Clear communication—stand-ups, async Loom walkthroughs, and well-commented PRs—keeps distributed teams unblocked. I am available 8 hours per day (40 hrs/week) for long-term, full-time remote work and can demo live projects or walk through my code at Github on request.
