Platforms that carry the business.
Cloud data platforms designed, migrated and run at telecom scale — GCP-first, engineering-led.
The estate grew one urgent project at a time. Now it's a warehouse past its limits, pipelines nobody wants to touch, and a cloud bill that rises faster than the value it produces. The board wants AI; the platform can barely serve last month's reporting.
- Platform architecture lake, warehouse and streaming layers designed as one system with ownership, contracts and cost controls, not a diagram that dies in Confluence.
- Migration at scale from on-premises Hadoop/Teradata estates to cloud-native platforms, run as waves with parallel-run verification. We've done this at the largest scale there is.
- Ingestion frameworks configurable batch and streaming ingestion (Beam, Spark, Kafka) so the hundredth feed costs a config file, not a project.
- Engineering standards CI/CD for data, testing, lineage, monitoring; the practices that make a platform run at 3am without waking anyone.
- Weeks 1–3 — Assessment Estate inventory, workload analysis, target architecture, migration sequence with effort and risk per wave.
- Then — Delivery We lead the build or the first waves, embed the framework, and hand a running system to your team — with your engineers pairing on it from week one.
Core architects on Vodafone's Neuron programme: 600+ on-premises servers across 11 countries migrated to Google Cloud, with a Scala/Beam framework serving 500+ daily feeds and 88 ML models. We replaced Sainsbury's Teradata warehouse with a multi-tenant lake handling 50+ source systems and 500+ daily jobs, and built the serverless AWS platform behind Virgin Money's digital bank.
GCP (BigQuery, Dataflow, Dataproc, Composer) · Apache Beam · Spark · Kafka · Airflow · dbt · Terraform · Scala · Python · AWS · Azure
Send us one diagram of the current estate. We'll come back with the three decisions that matter.
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