Case study · Series B SaaS

Data platform · SaaS & Technology · Data Engineering

A unified data platform for a high-growth SaaS company

Real-time ingestion from 15+ sources into a Snowflake warehouse, dbt models and executive dashboards with sub-200 ms queries.

PythonAirflowSnowflakedbtMetabaseAirbyte

  • 2.4M

    Records processed daily

  • <200 ms

    Dashboard query latency

  • 15+

    Data sources unified

  • 5

    Core systems shipped

The client

High-growth Series B SaaS platform

Rapid customer growth and an expanding product surface fragmented critical business data across 15+ tools, making a unified view of the business impossible.

The problem

No single source of truth for business decisions

Data lived in production databases, Stripe, HubSpot, Intercom, Salesforce and internal systems. Executives made growth decisions on stale spreadsheet exports.

The data team spent most of its time pulling and reconciling data. Board reports took a week; customer-health metrics were always a week behind.

  • Data scattered across 15+ disconnected tools
  • Decisions made on week-old spreadsheet exports
  • A full week to prepare each board report
  • No customer-health or churn prediction
  • Data team spending 80% of time wrangling
What we built

5 systems, one platform

Violet marks the parts where the AI does the work. A human approves every consequential change.

  1. Module 01

    Real-time ingestion layer

    Connectors for 15+ sources with change-data-capture for near-real-time sync without overloading source systems.

    • 15+ connectors
    • CDC sync
    • Incremental loads
    • Schema evolution
  2. Module 02

    Centralised warehouse

    Snowflake with raw, staging and production layers, lineage documentation and cost-optimised compute.

    • Three-layer architecture
    • Analytical query design
    • Lineage docs
    • Cost controls
  3. Module 03 AI does this

    Transformation & modelling

    dbt models for customer health, revenue, usage and churn — version-controlled with full test coverage.

    • dbt with tests
    • Health scoring
    • Revenue & churn metrics
    • Version control
  4. Module 04

    Executive dashboard suite

    Metabase dashboards for revenue, customer health, usage and ops KPIs, refreshing automatically at sub-200 ms.

    • Live ARR tracking
    • Churn-risk views
    • Usage analytics
    • Self-service exploration
  5. Module 05 AI does this

    Data quality & monitoring

    Schema validation, freshness monitoring, row-count assertions and anomaly detection on every pipeline run.

    • Quality assertions
    • Freshness alerts
    • Schema-change detection
    • Metric anomaly detection
Stack
PythonAirflowSnowflakedbtMetabaseAirbyteAWSDocker
Data Engineering·SaaS & Technology
Outcome

2.4M records processed daily.

More proof

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  • 50+

    Production systems shipped

  • 97%

    Client retention

  • 4–8 wk

    Typical time to production

  • 24h

    Reply from a senior engineer