Executive reporting only works when finance and product share the same definitions. We place analytics engineers who stabilize metric grain, maintain BI models, and keep dashboards trustworthy as the business grows.
Our approach
We start with the decisions your reports need to support — then match talent to your warehouse, dbt, and BI stack. Screening focuses on semantic clarity, stakeholder communication, and the discipline to document metrics before sprawling dashboards multiply.
Skills we staff
Profiles are matched to your warehouse, dbt, and BI tools.
- SQL & warehouse tuning — Warehouse SQL and query performance
- Looker / LookML — LookML models and governed explores
- Tableau — Dashboards for recurring executive use
- Power BI — Semantic models and Microsoft BI reports
- dbt analytics — dbt models and exposures for business logic
- Python analysis — Ad hoc analysis moved into maintained models
- Metric catalogs — Shared metric definitions and owners
- Metabase & self-serve BI — Self-serve BI with basic guardrails
- Reverse ETL (Census / Hightouch) — Metric syncs into CRM and product tools
Outcomes teams see
- 5+ years — Average analytics-engineering experience
- 2–4 weeks — Typical time to first trusted dashboard
- 50%+ — Reduction in duplicate reports across teams
- Documented metrics — Shared definitions stakeholders can reuse
Next step
Tell us which metrics matter and which BI tools you run. We set engagement terms and send analytics talent ready to align the numbers.