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SaaSSeries B SaaS Company

Building a Unified Data Platform for a High-Growth SaaS Company

From scattered data across 15+ tools to a unified platform processing 2.4M records daily with real-time executive dashboards and sub-200ms query latency.

High-Growth Series B SaaS Platform

A Series B SaaS company with rapid customer growth and an expanding product surface. As the company scaled, critical business data became fragmented across 15+ tools — making it impossible for executives to get a unified view of business performance without days of manual data wrangling.

No single source of truth for business decisions

Business data lived in 15+ disconnected tools — production databases, Stripe, HubSpot, Intercom, Salesforce, and various internal systems. There was no single source of truth, and executives were making critical growth decisions based on stale spreadsheet exports.

The data team spent most of their time pulling and reconciling data rather than analyzing it. Board reports required a full week of manual preparation, and customer health metrics were always at least a week behind.

Data scattered across 15+ disconnected tools

Executives making decisions on week-old spreadsheet exports

Board report preparation requiring a full week of manual work

No customer health scoring or churn prediction capability

Data team spending 80% of time on data wrangling vs. analysis

A complete modern data stack

Module 01

Real-Time Data Ingestion Layer

Automated ingestion pipelines pulling data from 15+ sources including PostgreSQL production databases, Stripe, HubSpot, Intercom, Salesforce, Google Analytics, and custom APIs. Change data capture ensures near-real-time sync without overloading source systems.

Key Capabilities

15+ source connectors (databases, SaaS APIs, webhooks)
Change data capture for real-time sync
Incremental loading to minimize API usage
Automatic schema detection and evolution
Module 02

Centralized Data Warehouse

Snowflake-based data warehouse with a well-architected schema designed for analytical workloads. Raw, staging, and production layers with clear data lineage and documentation. Optimized for the query patterns that power executive dashboards and operational reports.

Key Capabilities

Three-layer architecture (raw, staging, production)
Optimized for analytical query patterns
Complete data lineage documentation
Cost-optimized compute scaling
Module 03

Transformation & Modeling Layer

dbt-powered transformation layer that turns raw data into business-ready models. Customer health scores, revenue metrics, usage analytics, and churn predictions — all computed from unified data with full test coverage and version control.

Key Capabilities

dbt models with full test coverage
Customer health scoring computations
Revenue and churn metric calculations
Version-controlled transformations
Module 04

Executive Dashboard Suite

Metabase-powered dashboards delivering real-time visibility into business performance. Executives see revenue trends, customer health, product usage, and operational KPIs — all refreshing automatically with sub-200ms query response times.

Key Capabilities

Real-time revenue and ARR tracking
Customer health and churn risk dashboards
Product usage and adoption analytics
Self-service exploration for team leads
Module 05

Data Quality & Monitoring

Automated data quality checks running on every pipeline execution. Schema validation, freshness monitoring, row count assertions, and anomaly detection ensure the data powering decisions is always accurate and up to date.

Key Capabilities

Automated data quality assertions
Pipeline freshness monitoring and alerting
Schema change detection and notification
Anomaly detection on key business metrics

From days of data wrangling to real-time insights

2.4M

Records processed daily

<200ms

Query latency

15

Data sources unified

Real-time

Executive dashboards

PythonAirflowSnowflakedbtMetabaseAirbyteAWSDocker

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