C.1 · Data Engineering & Analytics
Data Platforms & Lakehouse Engineering
Lakehouse storage with schema evolution that does not break readers
What we build
Data platforms built for freshness and lineage. Lakehouse storage on Iceberg or Delta with schema evolution that does not break downstream readers, change-data-capture ingestion from operational systems without dual writes, batch and streaming transformation in one framework, columnar and time-series serving layers, cost-aware partitioning and compaction, and access control that follows the data rather than the tool.
Capabilities
- Lakehouse storage on Iceberg or Delta, with schema evolution readers survive
- Change-data-capture ingestion from operational systems, without dual writes
- Batch and streaming transformation on one framework and one set of definitions
- Partitioning, clustering, and compaction chosen from the measured query pattern
- Row and column level access control enforced at the storage layer
- Cost visibility per pipeline and per query, so the platform does not outgrow its value
Related services
How it connects
Where it sits in the stack.
This service, and the two it hands off to. None of them can be optimised alone.
Data Platforms
Data platforms built for freshness and lineage.
Business Intelligence
Reporting that a leadership team can actually argue from.
Data Engineering & Analytics · see serviceIntegration & Migration
Pipelines and migrations that can be verified rather than trusted.
Data Engineering & Analytics · see serviceBring us the whole problem.
Tell us where the work is stuck, whether that is a model that never reached production, an application nobody can change, a data platform nobody trusts, or a plant the business cannot see. An engineer replies with a first read, not a sales deck.