Keebo | Sightline Payments

Reclaiming Data Engineering Time: Sightline Payments Optimizes Snowflake with Keebo

Sightline offers debit and prepaid solutions that seamlessly integrate multiple funding sources, allowing consumers to quickly access and use their funds. The company relies on Snowflake to analyze data to support business decisions, enabling Sightline to attract more consumers, gather actionable feedback, and scale operations efficiently. 

By early 2023, Sightline recognized a critical need to control Snowflake costs and gain deeper insight into their data warehousing consumption. This effort was led by Sean Cooperman, Director of Data Engineering at Sightline. Cooperman brings more than 25 years of experience leading enterprise-scale data modernization, governance, and cloud transformation. He also helped deploy one of the first Snowflake instances in Florida nearly a decade ago, establishing scalable cloud data architecture and best practices.

The team initially used Snowflake console views to run queries that analyzed remote spillage and user-level consumption. They explored restructuring warehouses into different groups based on department, such as Marketing, Client Success, and Operations. But as business demands grew, manual monitoring became increasingly time-consuming, highlighting the need for a more systematic, automated solution.

Cooperman sought to build a strategy to manage warehouse spend at the compute layer. His goal was to reclaim the time data engineers spent on repetitive optimization tasks and redirect it toward higher-value work.

Sightline offers debit and prepaid solutions that seamlessly integrate multiple funding sources, allowing consumers to quickly access and use their funds. The company relies on Snowflake to analyze data to support business decisions, enabling Sightline to attract more consumers, gather actionable feedback, and scale operations efficiently. 

By early 2023, Sightline recognized a critical need to control Snowflake costs and gain deeper insight into their data warehousing consumption. This effort was led by Sean Cooperman, Director of Data Engineering at Sightline. Cooperman brings more than 25 years of experience leading enterprise-scale data modernization, governance, and cloud transformation. He also helped deploy one of the first Snowflake instances in Florida nearly a decade ago, establishing scalable cloud data architecture and best practices.

The team initially used Snowflake console views to run queries that analyzed remote spillage and user-level consumption. They explored restructuring warehouses into different groups based on department, such as Marketing, Client Success, and Operations. But as business demands grew, manual monitoring became increasingly time-consuming, highlighting the need for a more systematic, automated solution.

Cooperman sought to build a strategy to manage warehouse spend at the compute layer. His goal was to reclaim the time data engineers spent on repetitive optimization tasks and redirect it toward higher-value work.

Cooperman led the search for a solution to optimize Snowflake warehouses and discovered Keebo through a Google search. “Keebo really stood out to me. I read case studies online about how much it could save and its ease of use. I also found the licensing structure to be very fair.” 

The team evaluated Keebo to ensure it met their security requirements.

True to Keebo’s promise of simplicity, Sightline was able to implement Keebo quickly. “The documentation from Keebo is very well structured. The onboarding process allowed us to create a script internally and was mostly point-and-click. Within 10 minutes, we were up and running.” 

The team soon realized that Keebo required minimal effort to maintain. “In the beginning, I would log in to make sure Keebo was doing its job. Now, I log in once or twice a month.”

With Keebo, the Sightline team gained efficiency and control. Its autonomous optimization quickly identified misconfigured warehouses and allowed the team to leverage auto-suspend, resulting in better uptime. Keebo also eliminated manual tuning errors and established a unified governance layer, keeping Sightline’s optimization logic consistent, auditable, and scalable.

“Recently, our analysts were running queries that were not optimal, and thanks to Keebo, we were able to find the queries and fix them quickly with minimal data engineering effort. Keebo autonomously identifies what is wrong and provides the extra profiling we didn’t have before.” 

Reflecting on his goal to free up data engineering time, Cooperman noted:

By reclaiming those 15 hours a week, Sightline’s data engineering team was able to accelerate the company’s Medallion Architecture, focusing on the Bronze, Silver, and Gold data layers. This shift from constant warehouse monitoring to designing data flow became the primary driver of Sightline’s increased data reliability. The reclaimed bandwidth also enabled the implementation of a Data Mesh framework, empowering departments like Marketing and Operations to act as true data owners rather than waiting on a centralized engineering bottleneck.

Since 2023, Sightline has saved an average of 30-40% annually on Snowflake costs. These sustained savings have created headroom for innovation, enabling the team to invest in new initiatives without increasing the overall budget. 

These results mark a significant milestone in Sightline’s FinOps for Data Clouds journey, helping the company move from reactive budgeting to a proactive model where it can measure unit economics by department.

Drawing on his experience with Keebo, Cooperman shares advice for teams that may be skeptical about automated warehouse optimization:

“I was skeptical at first about it, too, but I would encourage people to definitely give it a try. Within three weeks of implementation, we were able to see the immediate impact. We had really great support. Our Keebo Customer Success Manager helped ensure we had the right balance on our settings and shared valuable information about how the solution works. The personal client touch made a real difference.”

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