Data Engineering
Raw data is worthless. We build the infrastructure that turns it into decisions.
We design and build data pipelines, warehouses, and analytics infrastructure that your team actually uses. Whether it's real-time streaming, batch ETL, or BI dashboards, we own the full stack — from ingestion to insight.
Product analytics
Event tracking, funnel analysis, and retention metrics — all in one queryable warehouse.
Revenue & financial reporting
Unified revenue data across Stripe, CRM, and billing tools — reconciled automatically.
Real-time operational dashboards
Live views of operations, inventory, or logistics — streaming, not daily exports.
ML feature stores
Reliable, versioned features for ML models — consistent between training and inference.
Data audit & architecture design
We map every data source you have — product events, CRM, third-party APIs — and design a unified architecture that makes all of it queryable, reliable, and maintainable.
Pipeline & ingestion layer
We build ingestion pipelines for batch and streaming data, with schema validation, deduplication, and error alerting. Data lands in your warehouse clean, not in need of further cleaning.
Transformation & modelling
We use dbt to build a layered transformation model — staging, intermediate, and mart layers — so your analysts can query trusted, documented datasets without understanding raw tables.
Dashboards & ongoing monitoring
We build dashboards in Metabase, Looker, or your preferred BI tool — connected to your warehouse, not to spreadsheets. We set up data quality monitoring so you know when something's wrong before your team does.
- ↗Data pipeline architecture & ETL
- ↗Data warehouse setup (BigQuery, Snowflake)
- ↗dbt transformation models & documentation
- ↗Real-time streaming (Kafka, Flink)
- ↗BI dashboards & reporting
- ↗Data quality monitoring & alerting
Do we need a data warehouse if we're still early-stage?
If you have more than one data source and more than one person asking questions about data — yes. Starting with a proper warehouse early is dramatically cheaper than migrating a mess later.
What's the difference between a data pipeline and a data warehouse?
A pipeline moves data from A to B. A warehouse is where data lands, gets transformed, and gets queried. You need both. We build the full stack.
Can you work with our existing BI tools?
Yes. We connect to Metabase, Looker, Tableau, Power BI, and most other BI tools. If you have existing dashboards, we can migrate them to point at clean warehouse data.
How do you handle data quality?
We build dbt tests, Great Expectations checks, and monitoring alerts into every pipeline. When data quality drops, you hear about it from us — not from your CEO asking why the numbers look wrong.
