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Snowflake Analytics for a Public SaaS Company

A Gartner-leader SaaS product company

A Snowflake warehouse serving internal BI, self-service, customer-facing insights and ML features.

Snowflake dbt Apache Kafka Fivetran Looker Tableau Apache Airflow Cube.js Terraform Amazon S3

The team

  • Data Platform team, ~8 engineers, managing the Kafka stream
  • Data Pipeline team, ~6 engineers, managing ingestion into Snowflake
  • Data Warehouse team, ~6 engineers, managing core DW models
  • Separate ML and BI teams
  • 1 Senior Manager and 1 VP of Data

Use cases

  • Data warehouse and BI reporting in Tableau
  • Self-service reporting in Looker and Snowsight
  • Customer-facing data insights
  • ML features for the product
  • Technical sales team dashboards

Architecture

Architecture with MongoDB, Postgres and product logs through Kafka to an S3 landing bucket, into Snowflake external, staging, core and datamart tables, out to Tableau and Looker
Kafka to S3 to Snowflake external tables, then staging, core and team datamarts serving Tableau, Looker and ML products.

What was built

  • Snowflake as the core data warehouse
  • dbt core for all Snowflake transformations
  • Kafka Connect writing MongoDB and application logs to S3, read through Snowflake external tables
  • Fivetran for Salesforce, Marketo, Google Sheets and similar sources
  • Looker for self-service BI, Tableau for curated BI
  • Cube.js powering customer-facing visualisations
  • Airflow for orchestration, with dbt running on ECS
  • Terraform for infrastructure and Snowflake itself, with Git and CI/CD

What changed

  • One warehouse served four distinct audiences: internal BI, self-service analysts, the product's own customers, and ML.
  • Splitting platform, pipeline and warehouse into separate teams scaled delivery — at the cost of fewer people holding the end-to-end picture.
  • Snowflake and its access model were managed in Terraform, so environment changes went through review like any other code.

Let's talk about your data platform

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