Data Engineering & Platforms

Platform Capability

Azure / AWS / GCP

Cloud foundations for production data, AI, and enterprise integration.

Discuss a Azure / AWS / GCP project

What it is

Azure, AWS, and GCP are not just hosting choices. They define how your data moves, how AI services are governed, how identity and networking are controlled, and how quickly teams can ship production systems. We design and build cloud data foundations that connect operational systems, analytics platforms, AI services, and security controls into one workable architecture.

When we recommend it

  • Your company already has a strategic Azure, AWS, or GCP commitment and needs the data and AI estate to follow it properly.
  • You need secure access to managed AI services such as Azure OpenAI, AWS Bedrock, SageMaker, or Vertex AI without breaking data governance.
  • You are connecting cloud platforms to on-premise ERP, CRM, file-transfer, warehouse, or operational systems.
  • You need the cloud foundation fixed before Snowflake, Databricks, BI, or AI workloads can run reliably.
  • Cloud data costs are growing faster than usage and you need architecture, observability, and ownership controls.
  • You are migrating from on-premise infrastructure and need a target architecture, cutover plan, and validation strategy before moving critical workloads.

What we deliver

Our Azure / AWS / GCP capabilities

Azure Enterprise Data Platforms

Azure Data Factory, Fabric, Synapse, Azure Databricks, Azure OpenAI, Purview, Key Vault, and private networking for Microsoft-heavy enterprise estates.

AWS Lakehouse & AI Foundations

S3 data lakes, Glue, Redshift, Athena, Lake Formation, SageMaker, Bedrock, Lambda, Step Functions, and event-driven data pipelines built for scale and cost control.

GCP Analytics & AI Platforms

BigQuery, Dataflow, Dataproc, Cloud Composer, Vertex AI, Looker, Pub/Sub, and governed data products for teams already invested in Google Cloud.

Landing Zones for Data & AI

Account and subscription structure, IAM, private endpoints, network segmentation, secrets management, audit logging, and environment separation before workloads go live.

Data Movement & Integration

Secure ingestion from ERP, CRM, files, APIs, streaming systems, and legacy databases into cloud warehouses, lakehouses, object stores, and downstream AI services.

Security, Governance & Compliance

Data classification, encryption, masking, access review, audit trails, retention policy, regional residency controls, and governance operating models.

FinOps & Performance Engineering

Warehouse sizing, cluster policy, storage lifecycle rules, reserved capacity, workload scheduling, observability, and cost attribution by team or product.

Migration & Modernisation

On-premise warehouse migration, Hadoop and Spark modernisation, SQL Server or Oracle offload, cloud cutover planning, rollback design, and parallel validation.

Implementation playbook

From cloud accounts to a production data and AI foundation

Cloud data work is not just provisioning services. We design the network, identity, security, deployment, observability, and data movement patterns that let Snowflake, Databricks, BI, and AI workloads run safely in production.

Discuss this architecture
01

Control-plane design

  • 01

    Cloud landing zones with account or subscription structure, network segmentation, private endpoints, secrets, and audit logging.

  • 02

    Data ingestion from ERP, CRM, SFTP, APIs, event streams, databases, object stores, and SaaS systems.

  • 03

    Warehouse, lakehouse, object storage, queue, orchestration, and serverless patterns selected by workload.

  • 04

    Security controls for encryption, KMS, IAM, role separation, data residency, policy enforcement, and evidence capture.

  • 05

    Observability, cost attribution, workload scheduling, deployment automation, and incident runbooks.

02

Cloud build programmes

  • Azure data estate buildout using Fabric, ADF, Synapse, Azure Databricks, Purview, and Azure OpenAI.

  • AWS data platform buildout using S3, Glue, Redshift, Athena, Lake Formation, Bedrock, and SageMaker.

  • GCP analytics and AI buildout using BigQuery, Dataflow, Pub/Sub, Vertex AI, Composer, and Looker.

  • Cloud migration and modernisation for on-premise warehouses, Hadoop, file pipelines, and custom databases.

03

Operational assets

  • Target cloud architecture and environment design.

  • Infrastructure-as-code modules or deployment runbooks for repeatable environments.

  • Secure ingestion, transformation, orchestration, and monitoring patterns.

  • Governance, residency, encryption, and access-control implementation.

  • FinOps dashboards and remediation plan for runaway cloud data costs.

Working with Azure / AWS / GCP?

Tell us where you are and what you are trying to build. We will be straight with you about whether Azure / AWS / GCP is the right tool for it.

Start a conversation