At BHP, I build and operate end-to-end ELT pipelines for finance data, from API ingestion through Snowflake models and reporting applications. This work improved query performance by 45%, maintained more than 99.9% data availability, and cut warehouse costs by 40%.
I also re-architected Databricks marketing pipelines into CI/CD-driven workflows, reducing pipeline failures by 40%, dashboard failures by 30%, and customer churn by 26%. For the Portfolio Management Office, I built a real-time dashboard and unified data model that saved more than 1,000 labor hours annually.
At AB InBev, I built a negotiation simulator that generated more than 25% of the Technology function’s EBITDA that year. I also developed marketing and sales KPI dashboards that increased Marketing ROI by 44%, and Azure-based Investor Relations models and dashboards that saved more than 500 specialist hours annually.
I work across Python, PySpark, SQL, Snowflake, dbt, Databricks, AWS, and Azure, with a focus on reliable data platforms and production quality. I’m currently pursuing a postgraduate program in Applied AI Engineering and have also shared Python, SQL, and Power BI learning resources with more than 300 people entering or advancing in data careers.
