Complex data.
Clear decisions.

A basic ELT pipeline with 2 to 3 data sources and a Snowflake warehouse is typically running in 2 to 3 weeks. Full data platform including semantic layer, governance, and dashboards: 8 to 12 weeks. The first dashboards are usually available within 2 weeks of the pipeline being live.

Snowflake for most enterprise workloads: better separation of storage and compute, simpler cost model, and best-in-class support for dbt. BigQuery if you're already on GCP, running event-heavy analytics, or need ML features (BQML, Gemini) tightly coupled to your warehouse. We'll recommend based on your workload profile and existing infrastructure.

We start with what you have. Most engagements are additive: we layer in dbt on top of an existing warehouse, connect Power BI to a new semantic layer, or add Great Expectations to an already-running pipeline. Wholesale migrations are an option but never the first recommendation.

PII classification happens at ingestion time, not as a retrofit. We tag columns in the data catalog (DataHub), enforce column-level masking in the warehouse, and apply role-based access control at the semantic layer. Nothing reaches a dashboard that isn't explicitly cleared to be there.

Full documentation, recorded walkthroughs, and a 30-day support window after go-live. We write dbt like engineers: every model has a description, column docs, and test coverage, so your team isn't inheriting a black box. We also run knowledge-transfer sessions so your team owns it after we leave.

Yes, we offer retainer-based support covering pipeline monitoring, performance tuning, new source integrations, and BI expansions. Most clients stay on a light retainer (4 to 8 hours/month) once the platform is stable. We can also do periodic health reviews without a standing retainer.