Our Client

A leading global SaaS company relies on financial and operational data to measure critical business metrics, including Monthly Recurring Revenue (MRR), Annual Recurring Revenue (ARR), Customer Lifetime Value (LTV), Customer Churn, and New Logo Acquisition. These insights are essential for executive decision-making, financial planning, and business performance management.

Its Financial Data Platform (FDP 2.0) integrates data from multiple enterprise applications, including Zuora, Salesforce, NetSuite, and Product Events, to provide a unified foundation for enterprise reporting and analytics.

Business Objective

As the Financial Data Platform evolved to meet growing business demands, the customer sought to modernize its data architecture to improve scalability, maintainability, and governance while adopting Snowflake best practices and fully leveraging the platform’s native capabilities for enterprise data engineering, analytics, and secure data sharing.

Infometry was engaged to assess, redesign, and modernize the existing Financial Data Platform by implementing a modular Snowflake architecture that simplifies data integration, accelerates financial reporting, strengthens governance, and establishes a scalable foundation for enterprise analytics and future business growth.

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Business Challenges

The existing Financial Data Platform had successfully supported the organization’s reporting requirements for several years. As the platform expanded to accommodate new business capabilities and increasing data volumes, the architecture presented opportunities for modernization to improve operational efficiency, scalability, and long-term maintainability.

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Key challenges included:

  • Extensive custom Python processing and tightly coupled SQL transformations increased implementation and maintenance effort.
  • Full data refreshes during synchronization resulted in longer processing times and unnecessary compute consumption.
  • Limited auditability and governance made data lineage, reconciliation, and operational monitoring more challenging.
  • The absence of a modular architecture and dimensional data model increased the complexity of introducing new business capabilities.
  • Operational processing and analytical reporting shared common datasets, limiting workload optimization.
  • Growing business demands required a more flexible architecture capable of supporting future expansion. 
  • The organization wanted to leverage Snowflake best practices, native architecture patterns, and secure data sharing capabilities.

Solution

Infometry successfully modernized the Financial Data Platform by redesigning the architecture using Snowflake best practices and modern data engineering principle 

The implementation included: 

  • Designed and implemented a layered Enterprise Data Lake (EDL), Enterprise Staging Layer (ESL), and Enterprise Data Warehouse (EDW) architecture to clearly separate ingestion, transformation, and consumption workloads.

  • Replaced full data refreshes with incremental data processing using control and audit frameworks, significantly improving pipeline performance and reducing Snowflake compute consumption.

  • Implemented a standardized dimensional data model with common facts and conformed dimensions to improve reporting consistency, simplify analytics, and support future enhancements.

  • Introduced Apache Airflow for enterprise workflow orchestration, providing centralized scheduling, dependency management, monitoring, alerting, and operational visibility across data pipelines.

  • Strengthened data governance through standardized data models, referential integrity, auditability, and end-to-end data lineage.

  • Optimized data integration across Zuora, Salesforce, NetSuite, and Product Events while eliminating redundant data movement and improving overall pipeline efficiency.

  • Leveraged Snowflake's cloud-native architecture and secure data sharing capabilities to build a scalable, high-performance enterprise data platform..

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Business Outcomes

As the Financial Data Platform evolved to meet growing business demands, the customer sought to modernize its data architecture to improve scalability, maintainability, and governance while adopting Snowflake best practices and fully leveraging the platform’s native capabilities for enterprise data engineering, analytics, and secure data sharing.

Infometry was engaged to assess, redesign, and modernize the existing Financial Data Platform by implementing a modular Snowflake architecture that simplifies data integration, accelerates financial reporting, strengthens governance, and establishes a scalable foundation for enterprise analytics and future business growth.

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improvement in data pipeline performance

by replacing full data refreshes with incremental data processing.

Reduced Snowflake compute consumption

through optimized data movement and incremental loading strategies.

Significantly improved maintainability

and operational support through modular architecture and Apache Airflow-based workflow orchestration.

Enhanced governance, auditability, and data quality

through standardized enterprise data models and comprehensive audit frameworks.

Accelerated delivery of new business requirements

by implementing reusable dimensional models and modular transformation pipelines.

Established a trusted enterprise reporting foundation

with standardized facts and conformed dimensions supporting consistent financial metrics across the organization.

Accelerated delivery of new business requirements

by implementing reusable dimensional models and modular transformation pipelines.

Technologies Used

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