Application Engineering
Web, mobile and backend software built to be maintained: clear architecture, tested code and interfaces that hold up as the business grows.
- React
- TypeScript
- Node.js
- .NET
- Python
Engineering intelligence. Powering digital transformation.
Engineering the digital infrastructure behind business growth.
From enterprise applications and cloud platforms to DevOps, AI/ML and data engineering, Data Cloud Engine helps businesses build secure, scalable and reliable digital capabilities.
One engineering partner from first design to daily operation.
Most technology problems sit between teams: the application works, but the platform does not suit it, or the data is right but cannot be reached securely. We work across those boundaries, so the whole system holds together.
- Architect
- Decisions on platform, data, identity and networking that will still make sense in three years, written down so your team can follow them.
- Engineer
- Applications, pipelines and infrastructure built as code, tested automatically and reviewed before they reach production.
- Secure
- Least privilege, private connectivity, policy checks and certificate automation designed in from the first environment.
- Operate
- Monitoring, upgrades and incident handling that keep systems dependable after launch, with fixes aimed at causes.
What we engineer
Eight service areas that cover the life of a digital platform, from the code customers use to the infrastructure and data underneath it.
Cloud Engineering
Architecture, migration and operations on Azure, AWS, Google Cloud, Oracle Cloud and IBM Cloud, designed for resilience and cost you can explain.
- Microsoft Azure
- Amazon Web Services
- Google Cloud Platform
- Oracle Cloud Infrastructure
- IBM Cloud
DevOps and Platform Engineering
CI/CD, GitOps, Kubernetes, OpenShift and service mesh engineering that makes releases routine and environments consistent.
- Docker
- Kubernetes
- Red Hat OpenShift
- Azure Kubernetes Service
- Azure Container Apps
AI, ML, MLOps and AIOps
The infrastructure and pipelines that take machine learning from a notebook to a monitored production service, and AI applied where it measurably helps.
- Python
- Kubernetes
- KServe
- MLflow
- Azure Machine Learning
Data Engineering and Database Management
Database architecture, migration, performance and data pipelines across MySQL, PostgreSQL, MongoDB and Azure Cosmos DB.
- MySQL
- PostgreSQL
- MongoDB
- Azure Cosmos DB
- MariaDB
Network and Cybersecurity Engineering
Secure network design, identity, TLS, container and Kubernetes security, built into infrastructure rather than bolted on.
- Kyverno
- Istio mTLS
- Keycloak
- Microsoft Entra ID
- cert-manager
API and Integration Management
API design, gateways, authentication and event-driven integration that connect your systems without turning them into a tangle.
- Azure API Management
- OpenAPI
- OAuth 2.0 / OIDC
- RabbitMQ
- Azure Service Bus
Managed Engineering and Technical Operations
Ongoing engineering support for the platforms and applications you depend on, scoped to what you actually need.
- Kubernetes
- OpenShift
- Azure
- AWS
- Prometheus
Cloud and platform engineering
We design landing zones, container platforms and delivery pipelines that let teams release often and recover quickly. Workloads go where they fit: Azure Container Apps for straightforward services, Kubernetes or OpenShift when you need operators, a service mesh or many teams on one platform.
- Azure
- AWS
- Kubernetes
- OpenShift
- Argo CD
- Istio
- Azure DevOps
- Infrastructure as code across Azure, AWS, GCP, OCI and IBM Cloud
- GitOps with Argo CD and policy as code with Kyverno
- Istio for traffic management and mutual TLS
- Upgrade plans that are rehearsed before production
Application engineering
Web, mobile and backend software with the structure to be changed safely. We choose languages and frameworks per component, document the reasoning and leave behind code your own developers can extend.
- React
- TypeScript
- Node.js
- .NET
- Python
- Java
- Enterprise web apps, corporate sites and progressive web apps
- Android, iOS and cross-platform mobile apps
- APIs and services in Node.js, .NET, Python or Java
- Staged modernisation of legacy systems
AI, ML and intelligent automation
Machine learning only pays off in production. We build the serving infrastructure, MLOps pipelines and monitoring that get models there, and apply automation to workflows where it measurably saves time.
- Python
- KServe
- MLflow
- Kubernetes
- Azure Machine Learning
- Model deployment, autoscaling and safe rollout
- Training and evaluation pipelines with traceable lineage
- Drift and quality monitoring
- AIOps built on clean telemetry, not guesswork
Solutions shaped by your industry
The same engineering disciplines, applied to the pressures of each sector: distribution chains, regulatory scrutiny, seasonal peaks or shop-floor data.
FMCG
Distribution apps, inventory visibility and order processing for fast-moving consumer goods.
View solutions for FMCGFinance and Financial Services
Secure applications, reliable infrastructure and auditable data processing for financial institutions.
View solutions for Finance and Financial ServicesGoods and Distribution
Order management, inventory systems and process automation for wholesalers and distributors.
View solutions for Goods and DistributionLogistics and Parcel Delivery
Shipment tracking, delivery workflows, partner APIs and operational dashboards.
View solutions for Logistics and Parcel DeliveryE-commerce
Customer-facing storefronts, backend services and infrastructure that scales with demand.
View solutions for E-commerceManufacturing and Industrial
Production dashboards, system integration and operational data platforms for industrial businesses.
View solutions for Manufacturing and Industrial
A technology ecosystem chosen per problem
We work across the major clouds, open-source platforms and languages. Listing a technology describes our experience with it, not a vendor partnership.
- Programming languages
- TypeScript and JavaScript
- Python
- Java
- C#
- Rust
- C++
- Databases
- MySQL
- PostgreSQL
- MongoDB
- Azure Cosmos DB
- MariaDB
- Redis / Valkey
- Cloud platforms
- Microsoft Azure
- Amazon Web Services
- Google Cloud Platform
- Oracle Cloud Infrastructure
- IBM Cloud
- Containers and orchestration
- Docker
- Kubernetes
- Red Hat OpenShift
- Azure Kubernetes Service
- Amazon EKS
- Google Kubernetes Engine
- Azure Container Apps
- DevOps and GitOps
- Azure DevOps
- GitHub Actions
- AWS CodeDeploy
- Argo CD
- Helm
- Terraform
- Ansible
- Security and policy
- Kyverno
- cert-manager
- Keycloak
- Microsoft Entra ID
- Azure Key Vault
- Trivy
Selected clients
We only publish client names and project details with permission. More references will be added as engagements are approved for publication.
gacontractors.in
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How an engagement runs
A predictable sequence, scaled to the size of the work. Small changes move through it in days; platform programmes in planned phases.
Discover
Understand the business goal, the systems in place and the constraints: budget, compliance, skills and timelines.
Design
Agree an architecture and delivery plan, with the trade-offs written down and the risks named early.
Build
Deliver in small, reviewed increments with automated tests, infrastructure as code and regular demonstrations.
Launch
Release through pipelines with monitoring, rollback paths and runbooks in place before go-live.
Operate and improve
Run, measure and refine, or hand over fully documented to your own team.
Why work with Data Cloud Engine
We are an engineering company first. The way we work is designed to leave you with systems you understand and can rely on.
Full-stack engineering depth
Applications, platforms, data and security handled by one team that understands how they affect each other.
Fit over fashion
We recommend the simplest platform that meets the need, even when that is less work for us.
Security as a default
Least privilege, private networking, policy checks and secret hygiene are where we start, not extras.
Nothing left undocumented
Architecture decisions, runbooks and handover notes are part of the deliverable, so your team is never locked in.
Frequently asked questions
What kinds of organisations do you work with?
Startups, small and mid-sized businesses and enterprise teams that need engineering depth for software, cloud infrastructure, DevOps, data or AI work. Engagements range from a focused assessment to building and operating a complete platform.
Which cloud platform should we use?
It depends on your existing contracts, compliance needs, team skills and workloads. We work across Azure, AWS, Google Cloud, Oracle Cloud and IBM Cloud, and will recommend the platform that fits rather than the one we prefer.
Do we need Kubernetes?
Often not. Managed container services such as Azure Container Apps run many web applications and APIs well without the cost of operating a cluster. Kubernetes or OpenShift becomes worthwhile when you need operators, a service mesh, fine-grained scheduling or many teams sharing a platform.
Can you work with our existing systems and team?
Yes. Most work starts from what is already running. We document what we find, agree what must keep working, and hand over with the knowledge your team needs to own the result.
How does an engagement usually start?
With a conversation about the problem, followed by a short discovery to understand the systems and constraints. From that we propose scope, approach and an estimate before any build work begins.
Do you provide ongoing support?
We offer managed engineering for platforms and applications. Coverage hours, response expectations and escalation are agreed per engagement and written into the contract.
Tell us what you are building
Share the problem, the systems involved and your timeline. An engineer will reply to discuss scope and next steps.
Request a consultation