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B2B SaaS · publicly traded
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
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.
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