Mastering FinTech Data Complexity to Transform Risk Management for Accelerated Revenue Growth

share story



SoFi, a member-centric digital financial services platform with over 5.2 million members, offers an all-in-one app for Lending and Financial Services. Amidst a recovering economy, rapid growth in SoFi’s Banking, Investing, and Credit Card products intensified the challenge of managing extensive operational data across diverse business units while upholding robust governance.

The increasing complexity and volume of data demanded a robust data management system to make information available in an analytical format for reporting and analysis and created an opportunity to streamline their ongoing Snowflake migration. SoFi partnered with evolv Consulting for a solution, leveraging their reputation and history of client success, and their comprehensive data management expertise.

tech stack used

action plan

evolv’s strategic approach began with cleaning and modeling the operational data of various business functions within Sofi to solve for data maintenance needs, while also ensuring future scalability with well-designed data architecture to enable a sustainable, smooth migration to Snowflake. The team identified a solution to implement team-based data marts for different products, resulting in compliant and functional data for each team.

This solution prioritized migrating any vital data necessary for maintaining compliance and reporting, customized data ingestion models to improve workflow efficiency, and utilized metadata for ML to capture changing risk factors and trends.

evolv also introduced a groundbreaking micro-metrics architecture* to minimize downtime and mitigate failure risks, optimizing operations and analytics, and streamlining the Snowflake migration with long-term ROI for SoFi.



Began with cleansing legacy data and structuring operational data to meet maintenance and scalability needs for the client. 


Migrated to Snowflake, merging any vital data that was necessary for maintaining compliance and reporting. 


Implemented micro-metrics architecture that reduced downtime, optimized operations, and simplified the Snowflake migration. 


92% reduction in refresh load times

with full refresh load times improved from 72 hours to 6 hours full refresh load times improved from 72 hours to 6 hours

99% decrease in time of detection

The time of detection of data anomalies improved from 24 hours to ~ 5 minutes

>100% reduction in data freshness load

Reduced data reporting time from 48 hours to 8-24 hours, creating fresher data.

*Learn more about how to revolutionize “metric-intensive” data mart processing in our blog post, here.

share story