Top results
Completed a key migration step 75% faster
Rebuilt a legacy CDP process in a two-week sprint instead of the estimated two months.
Cut experimentation time by 99%
Reduced A/B testing of transformation approaches from more than a week to just 15–20 minutes.
Built one source of truth for the enterprise
A unified data platform serving Security Benefit, parent company Eldridge, and sister company Zinnia.
Freed Snowflake credits for higher-value initiatives
Efficient pipeline design enables reinvestment into AI use cases.
Pain points
Legacy systems and entrenched processes blocked modernization: An Amazon-based CDP using Athena, Glue, and heavy Python coding (alongside SQL Server databases with outdated stored procedures and SSIS packages) created significant technical debt.
Lack of data masking and governance controls introduced security risk: Legacy platforms lacked consistent protections, making role-based access control (RBAC) and dynamic masking difficult to implement and enforce.
Fragmented data environment complicated integration. Financial data from SS&C, Nationwide, and Morningstar, plus operational data from Salesforce and Vonage, needed consistent governance across use cases ranging from digital enrollment to financial reporting.
Previous cloud-based transformation tool failed to meet requirements: It struggled with orchestration and transformation complexity, frustrating engineers and slowing delivery.
The challenge
The team needed to rebuild pipelines with stronger controls and faster iteration, without relying on undocumented logic or one-off scripts owned by a few individuals.
The data landscape supported a wide range of use cases, from digital enrollment and call-center scorecards to financial reporting and data science models. Security Benefit needed a Snowflake-aligned transformation approach that kept work transparent, auditable, and maintainable across feeds from five core systems.
The evaluation
Security Benefit evaluated platforms based on Snowflake execution fit, auditability, governance support, and how quickly engineers could change logic without breaking downstream dependencies. Leadership also favored an approach that was low-code, transparent, and fully customizable.
The team selected Coalesce for its ability to provide a clear, documented path from raw ingestion to governed data products.
The migration
To validate the platform, Young proposed a proof of concept designed to push Coalesce to its limits. “If you think it’s something we might have to do someday, test it—if we’re going to break it, let’s break it now,” she told data engineer Sachin Bathini. The POC included the most challenging sources, parsing and normalizing complex JSON structures in preparation for full-scale adoption.
Impressed by Coalesce’s speed and flexibility, the team formally adopted the platform in early 2025 and enrolled all engineers in the Coalesce Jumpstart program.
During the first major migration to decommission the CDP system, the team spent nearly two months documenting column usage and end-to-end dependencies in the legacy flow. They then rebuilt the process in a single two-week sprint, creating a repeatable migration blueprint.
The impact
With Snowflake at the center and Coalesce as the transformation platform, Security Benefit accelerated migration timelines while strengthening governance and day-to-day delivery.
Data teams ship changes faster and keep experimentation practical
Before standardizing on Coalesce, engineers worked across four distinct patterns: Python-heavy CDP processes, multiple AWS services, Snowflake SQL, and legacy SQL Server procedures. That fragmentation made logic harder to review, slowed experimentation, and increased operational risk.
After adoption, experimentation cycles dropped by roughly 90%. The team also reduced build time for a Snowflake-to-S3 CSV export with strict naming requirements to 4–5 hours, down from an estimated week of Lambda and Python work. By avoiding inefficient query patterns, Security Benefit preserved Snowflake credits for higher-value initiatives, including planned AI workstreams.
Teams build trust through lineage, documentation, and controlled access
Transparency was a requirement for Security Benefit. Leaders and business users needed to understand how metrics changed across data from five major systems. By standardizing transformations in Coalesce, the team enabled consistent lineage and built-in documentation—eliminating the need to chase logic across scripts and job configurations.
Data scientists can experiment freely in non-production environments, but once models are ready for deployment, they are productionalized in Coalesce.
A scalable foundation supports a shared enterprise source of truth
Security Benefit is building toward a single enterprise source of truth shared across the organization. That vision has driven an emphasis on repeatable patterns, consistent documentation, and clear ownership to ensure the platform scales without creating new silos.
By standardizing transformation development in Coalesce, the team reduced operational risk and dependency on a small number of individuals with institutional pipeline knowledge. Looking ahead, Security Benefit plans to continue retiring legacy systems and reinvest freed capacity into AI initiatives, including sales scoring, propensity modeling, and Salesforce optimization.