Cloud-Native Portfolio Analytics Platform Across 15+ Portfolio Companies

Deployed Snowflake on AWS to unify reporting across fragmented ERP, financial, and HR systems

Technology
Cloud Transformation
Geography
North America
Industry
Private Equity & Investment

Key results

Fast Multi-Entity Migration

Migrated data from 15+ portfolio companies to Snowflake in under 12 weeks

Accelerated Reporting Cycles

Reduced monthly reporting turnaround from 14 days to just 2 days

Unified Real-Time Visibility

Delivered a centralized, real-time dashboard layer across the entire portfolio

Future-Ready Data Foundation

Established a robust, cloud-native architecture to support scalable analytics and AI use cases

About the Client

A New York-based mid-market private equity firm with a diverse portfolio in healthcare, manufacturing, and services. The firm sought a cloud-native solution to unify operational and financial reporting from 15+ portfolio companies and enable faster, data-driven decisions.

Key Challenges

Inconsistent reporting formats across portfolio companies using disparate systems

Manual consolidation of spreadsheets from CFOs delayed reporting by weeks

Limited visibility into portfolio-wide KPIs such as revenue, margin, and headcount

Our Approach

  • Designed a cloud-native architecture using Snowflake on AWS
  • Built custom Python-based data pipelines to extract, normalize, and ingest data
  • Delivered a scalable reporting layer using Power BI for real-time portfolio visibility

Implemented Solution

Cloud Infrastructure Setup

  • Provisioned Snowflake on AWS as a centralized reporting layer

  • Configured scalable compute with usage-based controls and role-based security

  • Created isolated workspaces for each portfolio company

Custom Python-Based Data Integration

  • Built connectors for ERPs (NetSuite, SAP, MS Dynamics), finance tools, and HR platforms

  • Automated daily ingestion with validation, schema mapping, and retry logic

  • Logged all pipeline executions for auditability and data lineage

Data Transformation & Warehousing

  • Standardized key metrics like revenue, EBITDA, and headcount using modular Python scripts

  • Enabled flexible schema to support company-specific reporting nuances

  • Created clean, query-ready data sets for downstream reporting

Reporting Layer & Insights Delivery

  • Designed Power BI dashboards for finance, operations, and portfolio managers

  • Enabled drill-down by company, metric, and time period
  • Set up automated report refresh and delivery to internal stakeholders

Technologies Used

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