Most of my work is rebuilding broken data foundations, usually a migration off legacy BI, sometimes a fresh build + AI-readiness. The destination is a modern, AI-ready stack, sized for your scale: dbt + Snowflake + Fivetran + Omni at the high end, leaner alternatives for earlier-stage brands. The before-state is usually fragmented data, dashboards stakeholders don’t trust. The after-state is a scalable, trustworthy foundation with models and metrics everyone can make decisions off plus a governed semantic layer that is the trusted source of truth, queried by teams and AI agents.
Defining the metrics that matter — cohort retention, LTV, conversion, health outcomes — and getting them agreed-on across stakeholders. The hardest part of the job, and the one a clean stack is meant to deliver on.
Snowflake warehouses provisioned to scale with your growth — one platform for all your data, no silos to maintain.
Centralized data from your operational tools — payments, e-commerce, CRM, marketing — into Snowflake using Fivetran. Everything in one place, ready to model.
Fast, well-documented dbt models your team can extend. Or migration and cleanup of an existing dbt project that’s grown into spaghetti.
Legacy BI (Tableau, Looker, Mode) migrated to a modern, AI-ready stack — dbt + a semantic layer like Omni. Reports are fast, metrics stay consistent, and your data is ready for AI.