I build the transformation layer between raw warehouse tables and the metrics people actually rely on , modeling, testing, and documenting data so the numbers hold up under scrutiny.
I believe trustworthy data unlocks better decisions. Analytics engineering is where that trust gets built, modeling raw tables into datasets that are clean, tested, and documented enough for the rest of the business to rely on without double-checking.
My work lives in the warehouse: writing the transformation logic between raw sources and BI-ready tables, defining metrics once so they don't drift between dashboards, and making sure a broken model fails a test in CI, not a stakeholder's trust in the numbers.
Modeling raw source tables into clean, tested marts and orchestrating the jobs that keep them fresh, built with an eye for documentation and testing, not just getting numbers to load.
Turning statistical analysis into a metric people can explore themselves, from raw comparison data to a published, self-serve workbook stakeholders actually open.
Building the front-end layer analytics teams don't always get to own: lightweight internal tools and visualizations, validated with A/B testing instead of guesswork.
Have a data model to untangle, a metric that needs a single source of truth, or just want to talk shop? My inbox and LinkedIn are both open.