Practice 02 · Cloud & data engineering on AWS
We build AWS platforms your own team can operate.
We design, build and migrate production platforms on AWS for banks, manufacturers and government bodies. The work runs from architecture and landing zones through data platforms and the infrastructure AI workloads depend on, and every delivery includes the documentation, runbooks and training the in-house team needs to run it.
What we build
FoundationsLanding zones & platform architecture
Multi-account AWS environments with identity, network segmentation, guardrails and logging designed in from the first commit, structured against the AWS Well-Architected Framework and reviewed against it before go-live. Everything is defined as infrastructure as code, deployed through a pipeline, and reproducible in a new account without a console session.
DataData platforms & migrations
Lake and lakehouse architectures, batch and streaming ingestion, cataloguing, lineage and access control, and the migration of workloads and data estates onto them. Migrations are planned against the existing landscape first, executed in waves with rollback points, and measured on what is still running twelve months later.
AI workloadsProduction AI infrastructure
The serving, orchestration, evaluation and monitoring infrastructure that generative and predictive workloads run on, instrumented so that the governance practice has the signals it needs: identity on every call, version pinning, event capture at the decision point, and cost attribution per use case.
OperationsObservability, cost & resilience
Dashboards and alarms bound to runbooks rather than to individuals, cost allocation tagging and budget alerting from day one, and recovery objectives that are stated, tested and rehearsed rather than assumed. A platform is finished when it can be deployed repeatably, watched in operation, and scaled without alarm.
The handover is part of the build
Most migration failures are quiet ones: the platform freezes in the state it was left in when the delivery team rolled off, because the knowledge was never written down and the people who held it are gone. We treat the handover as a deliverable with acceptance criteria. Before we step back, your team performs a full deployment, a rollback and an incident drill without us in the room, working only from the documentation, runbooks and architecture decision records we leave behind. Training is mapped to the roles your people actually fill, from platform engineering to data operations, and is assessed on whether they can do the work rather than on certificates collected.
John Nathan
Co-founder · AI & data implementation lead
John is a cloud architect formerly of AWS Professional Services. He holds all thirteen AWS certifications and the AWS Golden Jacket, and has architected and delivered production migrations and integrations for major banks, manufacturers and government bodies. He has worked every side of an engagement, from writing the RFP through to the handover, and builds for the team that will inherit the system.
13× AWS Certified · AWS Golden Jacket · Formerly AWS Professional Services
Practitioner training in cloud and data engineering runs alongside delivery, pitched to the roles your team fills and delivered in-house or online. Tell us what you are building, and we will set out how we would build and hand it over.