Add finance_excellent_cohort_revenue model

dbt/models/marts/core/schema.yml

@@ -83,10 +83,19 @@ models:
     columns:
       - name: product_id
         description: Unique product identifier — primary key
+        data_type: varchar
         tests:
           - unique
           - not_null
 
+      - name: rating_tier
+        description: >
+          Business-logic classification of product quality derived from avg_rating.
+          Tiers are defined as: Excellent (avg_rating >= 4.5), Good (avg_rating >= 3.5),
+          Average (avg_rating >= 2.5), and Poor (avg_rating < 2.5). Used as the cohort
+          key for rating-based revenue and margin analysis (e.g. finance_excellent_cohort_revenue).
+        data_type: varchar
+
   # ────────────────────────────────────────────
   # dim_dates
   # ────────────────────────────────────────────

dbt/models/marts/finance/finance_excellent_cohort_revenue.sql

@@ -0,0 +1,49 @@
+-- Revenue metrics for the Excellent rating cohort (avg_rating >= 4.5)
+-- Filters dim_products to only Excellent-rated products and surfaces
+-- per-product revenue alongside cohort-level totals and share metrics.
+
+with excellent_products as (
+    select
+        product_id,
+        product_name,
+        product_category,
+        avg_rating,
+        rating_tier,
+        review_count,
+        positive_reviews,
+        negative_reviews,
+        unit_price,
+        unit_margin,
+        margin_pct,
+        total_units_sold,
+        product_total_revenue,
+        product_gross_profit
+    from {{ ref('dim_products') }}
+    where rating_tier = 'Excellent'
+),
+
+final as (
+    select
+        product_id,
+        product_name,
+        product_category,
+        avg_rating,
+        rating_tier,
+        review_count,
+        positive_reviews,
+        negative_reviews,
+        unit_price,
+        unit_margin,
+        margin_pct,
+        total_units_sold,
+        product_total_revenue,
+        product_gross_profit,
+        sum(product_total_revenue) over ()                                            as cohort_total_revenue,
+        round(
+            product_total_revenue / nullif(sum(product_total_revenue) over (), 0) * 100,
+            2
+        )                                                                             as pct_of_cohort_revenue
+    from excellent_products
+)
+
+select * from final

dbt/models/marts/finance/schema.yml

@@ -106,6 +106,155 @@ models:
       sla_hours: 3
       tier: 2
 
+  # ────────────────────────────────────────────
+  # finance_excellent_cohort_revenue
+  # ────────────────────────────────────────────
+  - name: finance_excellent_cohort_revenue
+    description: >
+      Revenue breakdown for the Excellent rating cohort — products with avg_rating >= 4.5
+      sourced from dim_products. Surfaces per-product revenue, gross profit, and margin
+      alongside the cohort's aggregate total revenue and each product's percentage share
+      of that total. Intended for finance and product teams to identify top-rated
+      revenue drivers and measure the financial weight of the highest-quality SKUs.
+    group: finance_analytics
+    access: public
+
+    config:
+      tags: ["active", "finance", "tier-1", "cohort"]
+      materialized: table
+
+    meta:
+      maintainer_email: marcus.chen@jaffle-shop.com
+      listeners:
+        - finance-team@jaffle-shop.com
+        - jordan.blake@jaffle-shop.com
+        - data-alerts@jaffle-shop.com
+      business_unit: "Finance"
+      model_type: "SQL"
+      schedule: "Daily at 07:00 AM UTC"
+      schedule_cron: "0 7 * * *"
+      status: "active"
+      revised_state: "RENEWED"
+      expiry_date: "2027-06-30"
+      approved: true
+      approved_by: marcus.chen@jaffle-shop.com
+      approved_date: "2024-06-01"
+      observe_in_airflow: true
+      sla_hours: 2
+      tier: 1
+      data_portal_tests:
+        - name: "All products are Excellent rating tier"
+          type: "accepted_values"
+          sql: "SELECT * FROM {{ model }} WHERE rating_tier != 'Excellent'"
+          expected_result: "0 rows"
+          description: "Ensures only Excellent-rated products (avg_rating >= 4.5) are present; any leakage from lower tiers would corrupt the cohort definition."
+        - name: "No negative product revenue"
+          type: "data_quality"
+          sql: "SELECT * FROM {{ model }} WHERE product_total_revenue < 0"
+          expected_result: "0 rows"
+          description: "Validates that no product carries a negative total revenue value, which would indicate a data pipeline error upstream."
+        - name: "avg_rating meets Excellent threshold on every row"
+          type: "accepted_values"
+          sql: "SELECT * FROM {{ model }} WHERE avg_rating < 4.5"
+          expected_result: "0 rows"
+          description: "Confirms that every product's avg_rating is at least 4.5, consistent with the Excellent tier definition in dim_products."
+        - name: "pct_of_cohort_revenue sums to 100"
+          type: "business_logic"
+          sql: "SELECT * FROM (SELECT ABS(SUM(pct_of_cohort_revenue) - 100) AS diff FROM {{ model }}) t WHERE diff > 0.01"
+          expected_result: "0 rows"
+          description: "Validates that pct_of_cohort_revenue values sum to 100% across all products, within a 0.01 rounding tolerance."
+        - name: "cohort_total_revenue is consistent across all rows"
+          type: "business_logic"
+          sql: "SELECT * FROM {{ model }} WHERE ABS(cohort_total_revenue - (SELECT SUM(product_total_revenue) FROM {{ model }})) > 0.01"
+          expected_result: "0 rows"
+          description: "Verifies that the cohort_total_revenue window value is identical on every row and equals the true sum of product_total_revenue."
+
+    columns:
+      - name: product_id
+        description: Unique product identifier — primary key, sourced from dim_products.
+        data_type: varchar
+        tests:
+          - unique
+          - not_null
+
+      - name: product_name
+        description: Human-readable product name, sourced from dim_products.
+        data_type: varchar
+
+      - name: product_category
+        description: Product category grouping (e.g. Electronics, Apparel), sourced from dim_products.
+        data_type: varchar
+
+      - name: avg_rating
+        description: Average customer review rating for the product on a 0–5 scale. All values in this model are >= 4.5 (Excellent tier).
+        data_type: float
+        tests:
+          - not_null
+
+      - name: rating_tier
+        description: >
+          Rating tier classification derived from avg_rating in dim_products
+          (Excellent >= 4.5, Good >= 3.5, Average >= 2.5, Poor < 2.5).
+          All rows in this model have rating_tier = 'Excellent'.
+        data_type: varchar
+        tests:
+          - not_null
+
+      - name: review_count
+        description: Total number of customer reviews submitted for the product.
+        data_type: integer
+
+      - name: positive_reviews
+        description: Count of reviews with a rating >= 4, indicating positive customer sentiment.
+        data_type: integer
+
+      - name: negative_reviews
+        description: Count of reviews with a rating <= 2, indicating negative customer sentiment.
+        data_type: integer
+
+      - name: unit_price
+        description: Per-unit selling price in USD.
+        data_type: numeric
+
+      - name: unit_margin
+        description: Per-unit profit margin in USD (unit_price minus supply_cost).
+        data_type: numeric
+
+      - name: margin_pct
+        description: Gross margin percentage calculated as (unit_price - supply_cost) / unit_price * 100.
+        data_type: numeric
+
+      - name: total_units_sold
+        description: Total quantity of this product sold across all historical orders.
+        data_type: integer
+
+      - name: product_total_revenue
+        description: Total revenue generated by this product across all orders (units_sold × unit_price).
+        data_type: numeric
+        tests:
+          - not_null
+
+      - name: product_gross_profit
+        description: Total gross profit for the product (product_total_revenue minus total cost of goods sold).
+        data_type: numeric
+
+      - name: cohort_total_revenue
+        description: >
+          Sum of product_total_revenue across all Excellent-rated products in this model.
+          Computed as a window function; the value is identical on every row and represents
+          the full revenue contribution of the Excellent cohort.
+        data_type: numeric
+        tests:
+          - not_null
+
+      - name: pct_of_cohort_revenue
+        description: >
+          Each product's share of the Excellent cohort's total revenue, expressed as a
+          percentage (0–100). Calculated as product_total_revenue / cohort_total_revenue * 100,
+          rounded to 2 decimal places. Useful for identifying which Excellent products
+          are the largest revenue contributors within the cohort.
+        data_type: numeric
+
   # ────────────────────────────────────────────
   # finance_product_margins
   # ────────────────────────────────────────────