Amazon Seller Data to Snowflake

Your data team already runs on Snowflake. You have dbt models, Tableau dashboards, and years of institutional knowledge. The last thing you need is another data silo. Nova bridges the gap between Amazon's chaotic APIs and your existing data stack, delivering clean, normalized seller data directly to Snowflake. Across the seller cockpits we operate, the patterns below are what we'd flag in a margin review.

Building an Amazon data pipeline from scratch takes 12-18 months and costs $300K+ in engineering time. Most projects fail before they deliver anything useful. The Selling Partner API is a maze of 20+ endpoints, aggressive rate limits, and constant schema changes.

This guide covers three paths to getting Amazon seller data into Snowflake: the hard way (DIY with SP-API), the medium way (ETL tools), and the fast way (pre-built solutions like Nova). Enterprise data teams with existing Snowflake investments will learn how to skip the pipeline build and start analyzing Amazon data in days instead of months.

Why Snowflake for Amazon Seller Analytics

Snowflake has become the data warehouse of choice for enterprise teams. For Amazon sellers and aggregators managing large portfolios, Snowflake offers distinct advantages:

The teams getting the most from Snowflake aren't just storing Amazon data. They're integrating it with other data sources: Shopify sales, warehouse inventory, marketing spend across channels. Snowflake makes this integration seamless.

The Amazon API Chaos: Why Building Your Own Pipeline Fails

Let's be honest about what "build it yourself" actually means. According to industry research, 80% of data projects fail to deliver business value. Amazon data pipelines are particularly brutal.

A realistic timeline for a DIY Amazon-to-Snowflake pipeline:

These numbers come from aggregators and brands who've tried it. The ones who succeed usually have dedicated data engineering teams of 3+ people. Solo sellers or small teams? The math doesn't work.

What Amazon Data You Can Load to Snowflake

Amazon's SP-API provides access to hundreds of data points across four main categories. With the right pipeline (or Nova), you get access to all of it:

3 Ways to Get Amazon Data into Snowflake

Your options range from full DIY to fully managed. Here's an honest comparison:

Option 1: Build with SP-API (Hard)

This is the path described above. You build everything from scratch using Amazon's Selling Partner API documentation.

Best for: Aggregators with $100M+ GMV and dedicated data engineering teams of 5+ people.

Option 2: ETL Tools Like Fivetran or Stitch (Medium)

ETL (Extract, Transform, Load) tools provide pre-built connectors for common APIs. They handle authentication and basic data extraction.

Best for: Brands with existing data teams and Fivetran/Stitch subscriptions who want to accelerate the build.

Option 3: Pre-Built Solutions Like Nova (Easy)

Instead of building or configuring pipelines, you connect your Amazon accounts and receive clean, modeled data in Snowflake with hourly refresh cycles.

Best for: any seller who wants Amazon data in Snowflake without the engineering overhead. Especially agencies managing multiple brands and aggregators who need data fast.

Snowflake vs BigQuery for Amazon Data

Both are excellent choices. Your decision should depend on your existing stack, not Amazon-specific requirements:

If you're already using Snowflake for other data sources, keep Amazon data there too. The value of a single source of truth outweighs any marginal differences between platforms. See our BigQuery guide If you're evaluating both options.

Building Analytics in Snowflake with Amazon Data

Once your Amazon data is in Snowflake, you can run any SQL query. Here are examples of analyses that aren't possible in Seller Central:

Example 1: True P&L by SKU

This query calculates net profit per SKU by combining revenue, all Amazon fees, and advertising spend. Essential for profit analysis:

SELECT 
  sku,
  SUM(revenue) AS gross_revenue,
  SUM(fba_fees + referral_fees + storage_fees) AS total_amazon_fees,
  SUM(ad_spend) AS advertising_cost,
  SUM(revenue) - SUM(fba_fees + referral_fees + storage_fees + ad_spend) AS contribution_margin
FROM your_database.amazon_data.daily_sku_metrics
WHERE date >= DATEADD(day, -30, CURRENT_DATE())
GROUP BY sku
ORDER BY contribution_margin DESC
LIMIT 50;

Example 2: Conversion Rate by Day of Week

Identify which days drive the highest conversion for bid adjustments:

SELECT 
  DAYNAME(date) AS day_of_week,
  AVG(unit_session_percentage) AS avg_conversion_rate,
  SUM(sessions) AS total_sessions,
  SUM(units_ordered) AS total_units
FROM your_database.amazon_data.traffic_metrics
WHERE date >= DATEADD(day, -90, CURRENT_DATE())
GROUP BY DAYNAME(date)
ORDER BY avg_conversion_rate DESC;

Example 3: Cross-Marketplace TACoS Comparison

Compare TACoS performance across US, UK, DE, and other marketplaces:

SELECT 
  marketplace,
  COUNT(DISTINCT asin) AS active_asins,
  SUM(revenue_usd) AS total_revenue_usd,
  AVG(profit_margin) AS avg_profit_margin,
  DIV0(SUM(ad_spend_usd), SUM(revenue_usd)) * 100 AS tacos_percentage
FROM your_database.amazon_data.marketplace_summary
WHERE date >= DATEADD(day, -30, CURRENT_DATE())
GROUP BY marketplace
ORDER BY total_revenue_usd DESC;

Integrating Amazon Data with dbt

If your team uses dbt (data build tool), Amazon data becomes just another source in your existing workflow. Here's how to structure your dbt project:

Example dbt Model: Amazon P&L Mart

-- models/marts/amazon/fct_amazon_daily_pnl.sql
{{ config(
    materialized='incremental',
    unique_key=['date', 'sku', 'marketplace'],
    cluster_by=['date']
) }}

WITH orders AS (
    SELECT * FROM {{ ref('stg_amazon__orders') }}
),

fees AS (
    SELECT * FROM {{ ref('stg_amazon__fees') }}
),

advertising AS (
    SELECT * FROM {{ ref('stg_amazon__advertising') }}
)

SELECT
    o.date,
    o.sku,
    o.marketplace,
    o.revenue,
    o.units_sold,
    COALESCE(f.total_fees, 0) AS amazon_fees,
    COALESCE(a.ad_spend, 0) AS ad_spend,
    o.cogs,
    o.revenue - COALESCE(f.total_fees, 0) - COALESCE(a.ad_spend, 0) - o.cogs AS net_profit
FROM orders o
LEFT JOIN fees f ON o.date = f.date AND o.sku = f.sku
LEFT JOIN advertising a ON o.date = a.date AND o.sku = a.sku

{% if is_incremental() %}
WHERE o.date >= (SELECT MAX(date) FROM {{ this }})
{% endif %}

Connecting Snowflake to Tableau, Looker & Power BI

With Amazon data in Snowflake, connecting to visualization tools is straightforward:

Tableau

Tableau has native Snowflake integration with optimized performance. See our Amazon Tableau Dashboard Guide for templates and visualization best practices:

• Direct connection using Snowflake ODBC driver
• Live queries or extracts for dashboard performance
• Row-level security passes through Snowflake roles

Looker

Looker connects natively to Snowflake. Define your LookML models once, and your team can explore Amazon data through a governed semantic layer.

Power BI

Microsoft Power BI connects to Snowflake using the certified connector:

• Import or DirectQuery modes available
• Single sign-on with Azure AD integration
• Scheduled refresh for automated updates

If you're building custom dashboards from scratch, check our Amazon Looker Studio Dashboard Guide for templates and best practices that apply to any BI tool.

Getting Started: Your Next Steps

Frequently Asked Questions

References

External resources referenced in this guide:

• Snowflake Data Cloud - Enterprise data warehouse platform
• Amazon Selling Partner API Documentation - Official Amazon SP-API reference
• dbt Snowflake Setup Guide - Official dbt documentation for Snowflake
• Tableau Snowflake Documentation - Tableau connector setup guide
• Fivetran Amazon Connector - ETL tool for Amazon data
• Gartner Data Quality Research - Industry research on data project success rates
• Nova Data API - Pre-built Amazon data pipeline to Snowflake/BigQuery