Hire Data Engineers

Their depth is concentrated in the modern lakehouse: Databricks and Snowflake, Spark and PySpark, Delta Lake, and the governance tooling around them. They have migrated legacy warehouses without stopping the business, which is a different and harder skill than building greenfield.

Most teams arrive here with a working pipeline that nobody trusts and a backlog of one-off fixes. The first weeks usually go into making failures visible — tests, lineage and alerting — before any rebuild is discussed, because rebuilding on top of unknown data quality just moves the problem. Once that groundwork exists, migrations and modelling work can proceed incrementally, and you can keep reporting running while the platform underneath it changes.

Typical stack

What they do

• Design lakehouse and warehouse architectures with clear bronze, silver and gold layers
• Build batch and streaming ingestion pipelines from operational systems and third-party APIs
• Model data for analytics so metrics are defined once rather than per dashboard
• Migrate legacy ETL and on-premise warehouses incrementally, without a big-bang cutover
• Add data quality tests, lineage and governance so failures surface before users notice
• Tune query and cluster cost, which is usually where lakehouse budgets quietly escape

Use cases we deliver

Related specialisms

Frequently asked questions