MS Data Science · AWS Certified Data Engineer. Currently automating warehouse operations at Ozcare Organics. Three-plus years building production data systems for Mondelez, Nestlé, L'Oréal and Asahi, from raw ingestion to the decisions they drive.
SQL pipelines. Python automation. ERP extraction and dbt models. LLM-powered agents. I build the full stack from raw data to business decisions. Visualisation is the garnish, the work is upstream.
Building the automation and data layer behind a warehouse operation. Odoo holds the operational record; I pull from it over XML-RPC into a central database and model it in dbt. Airflow orchestration and a reporting layer are the next stages, so the floor runs on scheduled pipelines instead of manual exports.
Owned the full lifecycle of reporting systems, from schema design through deployment. 15,000+ daily records, SQL/Python pipelines and Power BI dashboards used by operations, finance and C-suite for enterprise clients (~$2M Mondelez, Nestlé, L'Oréal).
Enterprise data migration to AWS. SQL extraction & validation, dataset reconciliation at scale. Built the validation workflows that caught errors before they reached downstream systems.
Partnered with operations, finance and C-suite to translate requirements into data products that get used.
SQL + Python pipelines with automated validation gates.
DAX KPI measures across financial, logistics & client performance.
Replaced Excel workflows with Python + SQL + Power Automate.
Anomaly detection, drift monitoring & systematic reconciliation.
Playwright scrapes Seek listings, GPT tailors my resume & cover letter per job, then a live dashboard tracks every application. Background schedulers monitor new listings continuously, with no manual applications. FastAPI + MongoDB backend, Next.js frontend, AWS Cognito auth.
AI inbox analysis over WhatsApp/SMS. Ask in natural language. Claude uses tool-use reasoning to search Gmail, summarise threads, and surface what matters.
End-to-end reporting for Mondelez, Nestlé, L'Oréal. 15,000+ daily records, automated Power BI dashboards across the business.
CNN-based audio classification detecting emotions from speech patterns, with a real-time inference pipeline.
A dashboard is only as trustworthy as the pipeline behind it. These are the rules the upstream work runs on.
Pipelines re-run safely. Same input, same output. No duplicate records, no manual cleanup after a failed job.
Quality gates sit between every stage. Bad data fails loudly and early, before it ever reaches a dashboard or a decision.
Drift monitoring, anomaly detection and systematic reconciliation run continuously, not bolted on after something breaks.
Every manual Excel step is a bug waiting to happen. SQL, Python and Power Automate replace the copy-paste entirely.
Every pipeline I ship exists to shorten the distance between a number and an action. Visualisation is the garnish. The work is upstream.
Looking for roles in data engineering, AI systems and analytics. Melbourne-based, open to relocation, full Australian work rights.