As a mid-stage startup in the database-as-a-service space, the client needed reliable answers to key business questions ranging from customer journey analysis, funnel conversion and retention to usage and cost profitability. The company had operational data, resource data and financial master data, but no unified analytics platform that could support billing, marketing and growth analysis from the same source of truth.
We established a greenfield analytics infrastructure based on Snowflake, Airbyte, Grafana and Hex.
The core architecture was:
flowchart LR
operational[Client transactional systems] --> airbyte[Airbyte ingestion]
finance[Resource and financial master data] --> airbyte
airbyte --> snowflake[Snowflake analytics warehouse]
snowflake --> models[Foundational domain models]
models --> grafana[Grafana operational dashboards]
models --> hex[Hex analysis and notebooks]
Snowflake acted as the analytics warehouse. Airbyte handled ingestion from source systems. Grafana supported operational and monitoring views, while Hex supported deeper analytical exploration with the product manager and business stakeholders.
In close collaboration with the product manager, I set up the ETL infrastructure to collect client transactional data and integrate it with resource and financial master data. I also helped define the core business domains and foundational data models used across billing, resource optimization, marketing and growth analytics.
The architecture favored a pragmatic layered model over a heavyweight platform build. A fully custom ingestion framework was considered unnecessary at this stage because Airbyte covered the integration needs with less operational overhead. A dashboard-only setup was also dismissed because the client needed reusable modeled data, not only visualizations.
- increase the resiliency of data pipelines using data profiling/data monitoring, which helped achieve KPI of 8-hour resolution for critical issues
- define the core business domains and set up foundational data models and transformations which were used for billing, resource optimization, marketing and growth analytics
The client received a working analytics foundation rather than isolated reports. The same modeled data could be reused for billing analysis, resource optimization, marketing reporting and growth analytics, reducing duplicated definitions across teams. Data profiling and monitoring improved pipeline resilience and supported the target of resolving critical issues within 8 hours.