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Build an end-to-end BI solution connecting to a warehouse (or DuckDB/Postgres as a free stand-in). Requirements: (1) model a STAR SCHEMA in SQL/dbt (staging -> marts) as the governed source, or a well-structured semantic model in the BI tool; (2) a metrics/SEMANTIC layer with certified, documented definitions (single source of truth); (3) a multi-page dashboard designed from real stakeholder requirements, with drill-down, appropriate charts, and clear data STORYTELLING; (4) DATA-QUALITY validation (reconcile totals to source, check grain, sanity checks) documented; (5) GOVERNANCE — row-level security and/or certified datasets; (6) at least one deeper ANALYSIS (cohort/funnel/driver) with a conclusion that respects correlation-vs-causation. Document the requirements, the metric definitions, and what you'd monitor for trust (freshness/quality).
A governed star-schema/semantic model (dbt staging->marts or BI model) with certified, documented, single-source metric definitions.
A requirement-driven multi-page dashboard with drill-down + storytelling; a deeper analysis (cohort/funnel/driver) with a causation-aware conclusion.
Documented validation (reconcile to source / grain / sanity checks); row-level security or certification; a trust-monitoring note (freshness/quality).
Use this README structure to make the work reviewable.
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# An end-to-end BI solution on a warehouse ## Goal Build an end-to-end BI solution connecting to a warehouse (or DuckDB/Postgres as a free stand-in). Requirements: (1) model a STAR SCHEMA in SQL/dbt (staging -> marts) as the governed source, or a well-structured semantic model in the BI tool; (2) a metrics/SEMANTIC layer with certified, documented definitions (single source of truth); (3) a multi-page dashboard designed from real stakeholder requirements, with drill-down, appropriate charts, and clear data STORYTELLING; (4) DATA-QUALITY validation (reconcile totals to source, check grain, sanity checks) documented; (5) GOVERNANCE — row-level security and/or certified datasets; (6) at least one deeper ANALYSIS (cohort/funnel/driver) with a conclusion that respects correlation-vs-causation. Document the requirements, the metric definitions, and what you'd monitor for trust (freshness/quality). ## Acceptance criteria - [ ] Model & semantic layer: A governed star-schema/semantic model (dbt staging->marts or BI model) with certified, documented, single-source metric definitions. - [ ] Dashboards & analysis: A requirement-driven multi-page dashboard with drill-down + storytelling; a deeper analysis (cohort/funnel/driver) with a causation-aware conclusion. - [ ] Quality & governance: Documented validation (reconcile to source / grain / sanity checks); row-level security or certification; a trust-monitoring note (freshness/quality). ## Verification - [ ] Document installation and run commands. - [ ] Record automated checks and their exact commands. - [ ] Add representative output, screenshots, or a short demo where useful. - [ ] Confirm failure paths and known constraints. ## Decision log ### Decision title - Context: - Choice: - Alternatives considered: - Tradeoffs: ## Evidence - Link each rubric criterion to the file, test, or artifact that demonstrates it. ## Known limitations - List what is intentionally out of scope and what should be improved next.