Cloud adoption is accelerating across every industry. Public cloud spending has been climbing year-over-year, and the demand for skilled professionals consistently outpaces supply. Whether you are just entering the field or looking to increase your market value, building the right combination of cloud skills is one of the most reliable career investments you can make.
The following five skill areas represent a strong foundation for cloud-focused DevOps engineers, platform engineers, and architects. They are not exhaustive, but they map closely to where employers are spending budget and where hiring demand has been consistently strong.
1. Cloud Security
Security is arguably the most critical cloud skill you can develop, and it is also one of the most misunderstood. A widespread misconception is that once workloads move to a major public cloud provider, security becomes the provider’s problem. This is not how it works.
Every major cloud provider — AWS, Microsoft Azure, and Google Cloud — operates under a shared responsibility model. The provider secures the underlying infrastructure: physical hardware, hypervisors, networking fabric, and the managed services layer. The customer remains responsible for everything built on top: data classification, identity and access management (IAM), network security groups, encryption at rest and in transit, and application-level controls.
Practically, this means cloud engineers need to understand how to configure and use the native security tooling offered by each platform — for example, AWS IAM policies, AWS Security Hub, Azure Security Center, and Azure Policy. Mistakes in these areas are a leading cause of cloud data breaches.
For professionals seeking a vendor-neutral, industry-recognised credential in this space, (ISC)² CCSP (Certified Cloud Security Professional) is well-regarded. It builds on concepts covered in the CISSP and addresses cloud-specific domains including cloud data security, infrastructure security, and legal compliance.
2. Machine Learning and AI on Cloud Platforms
Machine learning and AI have moved well beyond buzzword status. All three major cloud platforms now offer managed ML services that remove much of the infrastructure overhead from model training and deployment. AWS provides SageMaker, Azure offers Azure Machine Learning, and Google Cloud provides Vertex AI.
These managed services lower the barrier to entry significantly. Tutorials, sample notebooks, and pre-built model endpoints make it possible to get a first experiment running quickly. However, extracting real business value from these tools requires a grounding in data science fundamentals: understanding data preparation and feature engineering, evaluating model performance, and avoiding common pitfalls like overfitting.
For DevOps engineers, the adjacent skill of MLOps — applying CI/CD, version control, and monitoring practices to machine learning pipelines — is increasingly valuable. Being the engineer who can reliably deploy and maintain an ML model in production is a distinct and sought-after capability.
3. Serverless Architecture
Serverless computing allows developers to deploy code without managing servers, operating system patches, or capacity planning. The cloud provider handles scaling automatically — including scaling to zero when there is no traffic, which directly reduces cost.
AWS Lambda is the most widely deployed serverless compute service, and its ecosystem of integrations (API Gateway, S3 event triggers, DynamoDB Streams, EventBridge) makes it a realistic foundation for production workloads. Azure Functions and Google Cloud Functions offer comparable models.
Early concerns around vendor lock-in with serverless were valid. If all of your business logic is written against platform-specific bindings, migrating to another provider is painful. The practical mitigation is to keep function logic thin and isolate provider-specific integration code at the edges of your application. Frameworks like the Serverless Framework and AWS SAM provide some abstraction and improve portability.
Key skills to develop in this area include: event-driven design patterns, cold start mitigation strategies, distributed tracing (AWS X-Ray, Azure Application Insights), and infrastructure-as-code deployment of serverless resources.
4. Cloud Migration and Multi-Cloud Deployment
Moving existing workloads to the cloud is rarely straightforward. Applications built for on-premises environments often assume local networking, specific OS configurations, or monolithic deployment models that do not translate directly to cloud-native patterns. Engineers who can assess workloads, design migration strategies, and execute them with minimal downtime are consistently in demand.
Common migration approaches include lift-and-shift (rehosting), re-platforming (moving to managed services like RDS or Azure SQL), and refactoring (redesigning for cloud-native architecture). Each carries different risk and cost profiles, and selecting the right approach for a given workload requires both technical and business judgment.
At enterprise scale, multi-cloud deployments are increasingly common. Organisations may use AWS for compute-heavy workloads, Azure for Active Directory integration and Microsoft 365 connectivity, and Google Cloud for data analytics and AI services. This means engineers who hold knowledge and certifications across multiple platforms — rather than being deeply siloed in one — are better positioned to support enterprise architecture decisions.
Relevant certifications to consider: AWS Solutions Architect, Azure Administrator (AZ-104), and Google Cloud Professional Cloud Architect. Holding two or more of these demonstrates genuine cross-platform capability.
5. Automation and Infrastructure as Code
Automation is the skill that ties everything else together. Cloud infrastructure that is provisioned manually is difficult to reproduce, audit, or scale. Infrastructure as Code (IaC) — defining your infrastructure in version-controlled configuration files — is now a baseline expectation in professional cloud engineering roles.
The core tools to know include:
- Terraform — the most widely adopted cloud-agnostic IaC tool, supporting AWS, Azure, GCP, and dozens of other providers through a consistent HCL syntax.
- AWS CloudFormation / Azure Bicep / Google Cloud Deployment Manager — native IaC options that offer deep platform integration at the cost of portability.
- Ansible and Chef — configuration management tools useful for managing the state of compute instances and automating application deployment.
- Jenkins, GitHub Actions, GitLab CI — CI/CD pipelines that automate testing, building, and deploying cloud workloads.
Auto-scaling policies, automated monitoring and alerting (CloudWatch, Azure Monitor, Grafana), and self-healing infrastructure patterns all fall under this umbrella. Engineers who can design systems that operate reliably without manual intervention are valuable to any team running cloud workloads at scale.
Building Marketable, Cross-Platform Cloud Skills
The clearest pattern in cloud hiring is that cross-platform breadth combined with depth in at least one area is more valuable than narrow specialisation in a single provider. Engineers who understand the shared responsibility model on AWS but can also configure Azure networking or deploy a Terraform module against Google Cloud are easier to integrate into diverse teams and projects.
A practical learning path for a DevOps engineer looking to strengthen their cloud skills might look like this:
- Gain a foundational certification on your primary platform (AWS SAA, AZ-104, or equivalent).
- Learn Terraform to at least an intermediate level — provision real infrastructure, use remote state, and manage modules.
- Build a serverless project end-to-end: an event-driven pipeline or a REST API backed by Lambda or Azure Functions.
- Study the shared responsibility model and platform-specific security tooling for your primary provider.
- Add a second platform certification to broaden your market appeal.
The cloud skills gap remains real and persistent. Engineers who invest consistently in these areas position themselves well regardless of which specific technologies dominate any given hiring cycle.
Original article sourced from cloudcomputing-news.net (2018), updated and expanded for current relevance.