Data Engineering

Pipelines and platforms that the rest of your stack can actually depend on.

Migrations, implementations, and optimizations across the data platforms your business already runs on — built so the data engineers who inherit it later won't have to start over.

Snowflake

Implementation, modeling, and cost optimization.

We design Snowflake data models that scale with your query patterns, not against them, and tune compute usage so costs stay predictable as volume grows.

  • Migration from legacy warehouses with minimal downtime
  • Data modeling aligned to how your teams actually query
  • Warehouse sizing and cost governance reviews
Databricks

Lakehouse architecture and Unity Catalog rollouts.

From raw ingestion to curated ML-ready tables, we build lakehouse pipelines on Databricks that support both analytics and model training from the same governed source.

  • Medallion architecture tuned to your data volumes
  • Unity Catalog rollout for unified governance
  • ML pipeline integration for feature stores and training data
Microsoft Fabric

End-to-end onboarding, OneLake to Power BI.

We handle the full Fabric rollout — OneLake, Direct Lake mode, and Power BI integration — so reporting performance improves instead of just changing platforms.

  • OneLake setup and workspace structuring
  • Direct Lake configuration for faster reporting
  • Power BI integration and migration planning

Let's scope your next build.

Tell us where you're stuck and we'll tell you, plainly, whether AI is the right tool for it.