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30 min.

to create

critical revenue metrics



for custom code



required than previous data warehouse


Slack brings together people, data, and applications by providing a single platform where people can effectively work together, find information, and access hundreds of thousands of critical applications and services to do their best work. Slack is headquartered in San Francisco, CA, and has 14 offices around the world.






Use case: 

Reporting tech:



San Francisco, CA

Computer Software


Matillion ETL for Snowflake

Data warehouse modernization



The Business Systems Data unit at Slack aims to empower every team in the organization with accurate data, delivered in a timely way. But Slack’s existing data architecture was preventing it from achieving this goal. The existing data warehouse didn’t support incremental loads, so loading data was time-consuming. As a result, by the time teams received data, it was already around 30 hours old. Plus, the company’s ETL processes were too resource intensive. Teams were writing Airflow and Python scripts for ETL, but scripting required too much development time and was not scalable. To support the growth of Slack, the team needed a modern data warehouse and ETL solution.


For its modern data architecture, Slack selected Snowflake as its cloud data platform and Looker as a business intelligence tool. The company chose Matillion ETL for Snowflake as its data transformation tool. Matillion ETL for Snowflake is designed to take advantage of Snowflake’s architecture, so it was easy to get up and running. In fact, Slack completed a proof of concept in just a couple of weeks. One key advantage to Matillion ETL for Snowflake was the product’s up-front and predictive pricing. The team had immediate access to all of Matillion’s pre-built connectors for no additional cost. With Snowflake, Matillion ETL for Snowflake, and Looker in place, Slack developed various analytics dashboards and bots serving Sales, Marketing, Finance, and Recruiting business units, enabling the company to use data insights in an agile and meaningful way. The company also developed a bot that delivers key business metrics to executives to help in making key business decisions.


  • Reduces the need for custom code, lessening the burden on valuable development resources
  • Requires less maintenance and administration than the previous data warehouse and ETL solution so a team of just two people can manage the stack
  • Decreases time required to create critical revenue metrics from up to 6 hours to just 30 minutes


Because Matillion ETL for Snowflake is designed for Snowflake, it integrates easily and allows Slack to use all of the power and features of the Snowflake data platform. Using Matillion ETL’s pre-built connectors, Slack can easily load data from Salesforce, Workday, and marketing applications such as Pardot, Google Analytics, and Facebook. Reusable components and frameworks mean that extracting data from a source can be reduced from up to 10 workflows to just one. Additionally, the team used Matillion’s API Query component to reach even more external systems, like Greenhouse, from which Slack was pulling in recruiting data to combine with Workday data to provide insight for recruiting teams.

One of my team's key responsibilities is delivering business systems data to the broader organization so that employees are empowered to drive innovation. Using Matillion ETL for Snowflake makes it easier to do that. Vamsee Kata Manager, Platform Architecture & Ops| Slack