Education Data · Applied ML · Agentic Delivery
About
I’m Luis Faria, a software and data engineer based in Sydney. For 10+ years I’ve built systems that turn manual, messy processes into automated, measurable products - across healthcare, marketing agencies, tech, and education.
I led technical projects and delivered custom ERP/CRM platforms for a 20+ clinic healthcare group serving 1M+ client records, then built Konquista, a Django + Celery/Redis automation platform pushing 30K+ WhatsApp messages a month across clinics. That operating background still shapes how I build: ship the system, measure the outcome, own the handover.
Today I work as a Data & Systems Specialist at St Catherine’s School, Sydney. The public-safe version: Next.js products, SQL Server pipelines, Power BI reporting, academic data modernisation, and cross-system integrations where privacy and authorization are non-negotiable.
I’m completing a Master of Software Engineering with AI, and the coursework keeps becoming shipped software: ReviewPulse for inspectable sentiment analysis, Sommelier API for model governance and deployment trade-offs, PySpark churn analysis, secure cloud architecture, and an agentic study pipeline documented in public.
Sydney, Australia
Currently
Building secure data systems at St Catherine’s School, Sydney - Next.js products, SQL Server pipelines, Power BI reporting, and academic data modernisation - while completing a Master of Software Engineering with AI. I keep the coursework applied: ReviewPulse for inspectable sentiment analysis, Sommelier API for model governance, churn modelling with PySpark, secure cloud architectures, and LLM/agent workflows - much of it open-sourced in my Master’s repo (1,400+ commits, mostly Jupyter notebooks).
What I do
Secure education systems
School-facing products and internal tools where authorization, privacy, auditability, and handover are part of the design - not cleanup work after launch.
Data engineering & analytics
SQL Server, Power BI, ETL, and reporting workflows built close to operations: finance, enrolments, academic data, support, and stakeholder dashboards.
Applied ML systems
Models handled like production assets: evaluation contracts, artifact provenance, failure visibility, and interfaces that make trade-offs inspectable.
Agentic AI delivery
Claude Code and Codex workflows used with explicit source truth, review gates, redaction, and public write-ups showing what the agent did and what stayed human-owned.
Tools I work with
Production systems
Data & analytics
Applied ML
Delivery & agents