End-to-End Modern Data Stack Implementation
Built the full data stack from scratch for a 5M+ MAU platform with no prior infrastructure.
Prior to this engagement, Stage.in lacked a coherent, production-grade data infrastructure. Data was fragmented across sources with no unified ingestion layer, no standardised transformation pipeline, and no reliable analytics-ready layer for the growing data team to build on.
Design and implement a full modern data stack from source connectivity through to analytics-ready data models — robust enough to support a 5M+ MAU platform and a data organisation being built from scratch at the same time.
Stage.in needed an entire data capability built simultaneously across five layers: product analytics instrumentation from scratch, a data warehouse, core reporting models, reverse ETL for segmentation and experimentation, and BI dashboards for content, executive/investor, and marketing teams — all with a lean, relatively inexperienced team and a real budget constraint. The stack had to be lightweight enough to be configured by one person and maintained by a small team without constant upkeep, while being robust enough to support a 5M+ MAU platform.
Rudderstack was chosen because it handled both event streaming and reverse ETL in a single tool, avoiding an additional vendor. Snowflake provided the warehouse layer with enough headroom for the company's growth stage. dbt handled transformation with built-in documentation. Metabase served as the BI layer — approachable enough for non-technical stakeholders to use independently once the initial dashboards were built.
- 01Mapped the full scope of requirements across all five layers with team capacity and budget as explicit design inputs; selected the final stack: Rudderstack, Snowflake, dbt, Metabase.
- 02Designed the event taxonomy and tracking framework from scratch; implemented Rudderstack for real-time event streaming and configured the reverse ETL pipeline for segmentation and experimentation use cases.
- 03Configured Snowflake as the warehouse: schema designed from raw ingestion through staging and curated layers, with documented grain and business logic at each level.
- 04Built core dbt models covering all key reporting entities: content performance, user behaviour, and business metrics required for executive and investor reporting.
- 05Configured Metabase and built dashboards for content, executive/investor, and marketing teams — designed to be maintained by non-technical stakeholders once handed over.
- 06Documented every component and established the conventions the data team would follow to extend the stack as they grew.
- Production data stack live across all five layers: instrumentation, streaming, warehousing, transformation, and BI
- Clean, documented schema from raw ingestion through to curated models serving as the foundation for all analytics and AI work
- Reverse ETL pipeline operational for segmentation and experimentation use cases without an additional vendor
- Data team unblocked to build on a reliable, scalable platform from day one
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