Databricks

Modernizing Data Infrastructure for a Leading Retirement Plan Recordkeeper

See how a leading retirement plan record keeper partnered with v4c.ai and Databricks to unify data, enable real-time insights, and scale governance. With a modern Lakehouse architecture and automated pipelines, they transformed decision-making across the business.

‍Client Overview

The client is one of the fastest-growing independent retirement plan recordkeepers in the U.S., managing mission-critical data operations to support member analytics, financial reporting, revenue management, and operational visibility. As the business scaled, their legacy data infrastructure began to show signs of strain, impacting performance, governance, and the ability to extract insights on time.

To support long-term growth and enable a shift toward data-driven decision making, the client partnered with v4c.ai to modernize its data platform architecture, unify data governance, and unlock real-time analytics across business functions.

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Challenge

Despite being a data-intensive organization, the client’s technology stack lacked the maturity needed for enterprise-scale analytics:

  • Fragmented Data Landscape: Critical data was spread across SQL Server, Oracle, Salesforce, and Excel, with no unified view or integration strategy.
  • Legacy Tools and Manual Deployments: The organization relied on Azure Data Factory with basic blob storage output, lacking automation, version control, or scalable deployment practices.
  • Limited Governance and Identity Controls: There was no enterprise-grade role-based access control (RBAC), lineage tracking, or identity integration, resulting in departmental silos and inconsistent data handling.
  • Inflexible Infrastructure: The existing bronze-level data lake limited the ability to perform advanced analytics or scale to new business use cases.

To overcome these bottlenecks, the client needed a future-ready architecture that could centralize data, enforce governance, and support scalable analytics, all while integrating with existing tools and workflows.

Solution

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v4c.ai led a full-stack modernization effort centered around the Databricks Lakehouse Platform, designed to deliver a unified architecture for data ingestion, processing, governance, and analytics. The engagement followed a phased approach:

Platform Foundation and Infrastructure as Code:

To enable a repeatable, secure environment for development and deployment, v4c.ai established core infrastructure automation:

  • Deployed Terraform-based infrastructure across DEV, UAT, and PROD environments to enforce consistency and accelerate deployments
  • Set up Unity Catalog for centralized metadata management and RBAC integration with Microsoft Entra ID
  • Designed and delivered a data stewardship enablement program to support adoption and long-term governance

Data Integration and Engineering:

The client’s legacy ETL stack was replaced with a scalable, modular ingestion framework supporting real-time and batch workloads:

  • Replaced Azure Data Factory with Lakeflow connectors for Salesforce, Oracle, SQL Server, and HubSpot
  • Built a full Medallion Architecture with automated bronze, silver, and gold pipelines using Delta Lake for ACID-compliant, versioned data
  • Enabled Salesforce Data Cloud integration using Change Data Capture (CDC) to power near real-time data updates

Analytics and Governance Delivery:

With the new data foundation in place, v4c.ai operationalized analytics and observability to support day-to-day decision-making:

  • Integrated Sigma BI for scalable self-service analytics across departments
  • Implemented governance dashboards with lineage, permissions, and usage tracking
  • Delivered a data quality monitoring framework using Grafana for real-time observability and proactive alerts

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Impact‍

Through platform modernization and unified governance, the client realized several measurable outcomes:

  • Unified Member View: Integrated data pipelines now provide a 360° view of member data, enabling more targeted campaigns and service personalization
  • Improved Decision-Making: Self-service dashboards and real-time reporting reduced reliance on manual data pulls and static reports
  • Scalability and Reliability: Delta Lake and medallion architecture support enterprise-grade workloads with transactional integrity and time travel capabilities
  • Governance at Scale: Unity Catalog and Entra ID integration ensure secure, compliant data access across business units
  • Faster Delivery Cycles: Infrastructure as Code via Terraform and CI/CD pipelines through GitHub enabled repeatable, testable deployments

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Conclusion

This engagement demonstrates how strategic platform modernization, anchored on Databricks Lakehouse and infrastructure as code, can unlock real-time insights and governance at enterprise scale. By replacing outdated workflows with automated pipelines, unified access controls, and modern analytics tooling, the client has built a foundation for long-term agility and data-driven growth.

v4c.ai served as an embedded partner throughout the engagement, designing architecture, operationalizing tools, and enabling teams to confidently own and scale their data platform.

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