Cloud, Data & DevOps
Infrastructure your team can change on a Friday afternoon
Cloud architecture, delivery pipelines, data platforms and observability — engineered for reliability, cost control and safe, frequent releases.
What this practice covers
The problem
What usually brings people here
If two or more of these describe your situation, they are probably the same underlying problem.
Infrastructure exists only in a console
Resources were created by hand over years. Nobody can recreate the environment, and nobody is confident enough to change it.
The cloud bill grows faster than usage
Over-provisioned instances, forgotten environments and egress charges nobody has traced. Without per-service attribution, cost reduction is guesswork.
Deploys are manual, so they are scary
Releases need a specific person, a specific evening and a written list of steps. That is a single point of failure with a calendar invite.
Reporting fights with production
Analytics queries run against the transactional database, so a dashboard refresh slows down the product.
Our approach
How we run cloud, data & devops work
We treat infrastructure as a product with users: your engineers. If shipping requires tribal knowledge or an out-of-hours window, the platform is not finished, regardless of how sophisticated it is.
Work is incremental and reversible. We codify what exists before changing it, add automated verification, then improve — so you are never in a state where the old path is gone and the new one is not proven.
Cost, reliability and delivery speed are treated as one system. Making deploys safe usually makes them more frequent, which makes each change smaller, which is what actually improves reliability.
Capabilities
What we actually do
Each capability below is a service you can engage on its own or as part of a broader programme.
Cloud Engineering
Cloud architecture defined entirely in code, with environments that can be recreated from a clean account and cost attributed to the teams and services that generate it.
- Landing zone, account structure and network design
- Terraform or Pulumi modules with reusable environment definitions
- Autoscaling, rightsizing and cost allocation tagging
- Backup, restore rehearsal and disaster recovery planning
DevOps and CI/CD
Pipelines that make releasing routine: automated tests, preview environments, progressive delivery and a rollback that takes one action rather than one meeting.
- Build, test and deploy pipelines with caching and parallelism
- Ephemeral preview environments per pull request
- Blue-green and canary deployment strategies
- Secret management, artefact signing and supply-chain checks
Data Engineering
Pipelines that move data reliably from source systems into a modelled warehouse, with tested transformations and quality checks that fail loudly.
- Batch and streaming ingestion with schema evolution handling
- Warehouse modelling in dbt with tested transformations
- Data quality checks, freshness monitoring and lineage
- Change data capture from operational databases
Analytics and BI
A semantic layer and dashboards built on agreed definitions, so two teams asking the same question get the same number.
- Metric definitions maintained in version control
- Executive, operational and self-service dashboards
- Product analytics instrumentation and event taxonomy
- Embedded, per-tenant analytics inside your own product
MLOps and Observability
The operational layer for models and services alike: reproducible deployment, drift and quality monitoring, and telemetry that shortens incidents.
- Model registry, versioning and reproducible training runs
- Feature stores and consistent offline/online serving
- Distributed tracing, structured logs and useful metrics
- Service level objectives with alerting tied to real user impact
Use cases
Situations we are asked about most
A team stuck on monthly release windows
Automated pipelines, preview environments and progressive rollout that let the same team release safely on a normal weekday.
A cloud bill nobody can explain
Tagging, per-service cost attribution, rightsizing and commitment planning — with reporting that keeps the savings from quietly eroding.
Reporting that slows the product down
Change data capture into a warehouse, modelled transformations, and dashboards that no longer touch the transactional database.
An incident process that starts with guessing
Tracing, structured logs, service level objectives and alerts tied to user impact rather than machine metrics.
A migration from on-premise to cloud
Assessment, phased migration with rollback, and a landing zone that meets your security requirements from the first workload.
Deliverables
What you get, concretely
Everything below is handed over as part of the engagement, not sold separately afterwards.
- Infrastructure as code covering every environment, in your repository
- CI/CD pipelines with automated tests and one-action rollback
- Monitoring dashboards, alert rules and service level objectives
- Cost attribution reporting with an actioned optimisation list
- Data platform with modelled, tested transformations
- Runbooks for deployment, incident response and disaster recovery
- Enablement sessions so your team operates it confidently
Delivery process
Assessment
Document what exists, how it is deployed and where the real risk sits, with a prioritised findings list.
- Current-state architecture
- Risk register
- Prioritised plan
Codify
Bring existing infrastructure under code before changing it, so every later step is reviewable and reversible.
- Terraform modules
- Environment definitions
- State management
Automate delivery
Build the pipeline, add test gates, and make rollback a single action.
- CI/CD pipelines
- Preview environments
- Rollback procedure
Instrument
Add tracing, logging, metrics and service level objectives so behaviour is visible before it is optimised.
- Telemetry
- Dashboards
- Alert policy
Optimise
Rightsize, tune, and remove waste against measured baselines rather than assumptions.
- Cost report
- Performance improvements
- Capacity plan
Enable
Documentation, runbooks and working sessions so your team owns the platform rather than depending on us.
- Runbooks
- Training sessions
- Handover
Technology
The stack behind this practice
Selected per engagement. We recommend based on your team and constraints, not on preference.
Cloud
- AWS
- Google Cloud
- Azure
- Cloudflare
- Vercel
Infrastructure as code
- Terraform
- Pulumi
- AWS CDK
- Helm
- Ansible
Delivery
- GitHub Actions
- GitLab CI
- ArgoCD
- Docker
- Kubernetes
Data
- Snowflake
- BigQuery
- Redshift
- ClickHouse
- dbt
- Airflow
- Kafka
- Debezium
Observability
- OpenTelemetry
- Grafana
- Prometheus
- Datadog
- Sentry
Security & quality
Non-negotiables on every engagement
- Identity-based access with short-lived credentials instead of long-lived keys
- Network segmentation and private connectivity for data stores
- Policy-as-code checks that block non-compliant infrastructure at pull request time
- Encrypted backups with periodic restore rehearsals — an untested backup is not a backup
- Centralised audit logging with tamper-evident retention
- Vulnerability scanning across images, dependencies and infrastructure definitions
Related work
A worked example
An illustrative engagement showing how this practice runs end to end.
- FinTechSample content
Multi-tenant billing and entitlements for a growing SaaS platform
Retrofitting organisations, entitlements and usage-based billing into a product originally built for individual users.
- Next.js
- TypeScript
- Node.js
- PostgreSQL
Modernize Legacy Applications
Incremental modernization using the strangler pattern — assessment, a test safety net, capability-by-capability migration, and no big-bang cutover.
See the approachAutomate Business Workflows
Event-driven automation across your existing systems — intake, classification, routing, approval and reporting, with exception handling that a business owner can operate.
See the approach
Engagement options
How to engage this practice
Dedicated Developers
A team that needs specific skills and already has engineering management in place.
Individual engineers who join your team full time, work in your repository and your process, and report to your leads. You direct the work day to day.
- Managed by
- You
- Commitment
- Monthly, typically three months minimum
Team Extension
Scaling an existing team quickly while keeping product direction fully in-house.
A group of engineers integrated into your existing team structure. Your leads set priorities and run the process; we handle recruitment, retention, performance and continuity.
- Managed by
- You, with our engineering support behind the team
- Commitment
- Monthly, typically three months minimum
Managed Delivery Pods
Owning an outcome end to end when you do not have management capacity to spare.
A cross-functional pod — engineers, QA, design and a delivery lead — that takes a defined scope and runs it. You set priorities and review outcomes; we run the delivery.
- Managed by
- Xalicon
- Commitment
- Quarterly, aligned to a defined scope
Offshore Development Centre
Building a durable long-term engineering capability outside your home market.
A dedicated long-term team operating as your extended engineering function, with its own hiring plan, career development and delivery structure aligned to your organisation.
- Managed by
- Shared governance between your leadership and ours
- Commitment
- Annual, with a defined growth plan
Questions
Cloud, Data & DevOps — questions we are asked
Cloud, Data & DevOps
Start a cloud, data & devops engagement
Tell us the outcome you need. We will tell you what it takes, what it does not, and where we would push back.
Prefer email? contact@xalicon.co