Data Engineering

A data warehouse stores structured, processed data optimised for SQL queries and BI reporting — ideal for finance, sales, and operations dashboards. A data lake stores raw data in any format at low cost, designed for large-scale processing and ML workloads. Many modern architectures use both, often in a lakehouse pattern that combines the flexibility of a lake with the query performance of a warehouse.

We implement validation rules at ingestion (schema checks, null rate monitoring, referential integrity), transformation tests (row count reconciliation, business rule assertions), and output alerting (dashboards that flag anomalies before they reach end users). We also set up data contracts between upstream producers and downstream consumers to catch breaking changes early.

Yes — we work across AWS, Azure, and Google Cloud, and we adapt to the tools you already have. If you're already using Redshift, we build on it rather than recommending a migration unless there's a clear performance or cost reason to move. Our goal is to improve your data platform, not sell you a greenfield rewrite.

A focused project — for example, migrating three reporting pipelines to a new warehouse — typically takes 6–10 weeks. A full data platform build with multiple source integrations, streaming, and a semantic layer usually runs 3–6 months. We scope precisely after the discovery audit so you always know what you're committing to.

Yes — maintainability is a design requirement, not an afterthought. We deliver clear documentation, use well-supported open tools rather than proprietary black boxes, and run knowledge-transfer sessions with your team. We also offer managed operations if you'd prefer us to continue running and improving the platform on an ongoing basis.