Google Cloud skills
employers want

Google Cloud fundamentals start with the resource hierarchy: organisation, folders and projects, which group resources, IAM and billing. Core services include Compute Engine (VMs), Cloud Run (managed containers), Google Kubernetes Engine (GKE), Cloud Functions, Cloud Storage, Cloud SQL and other databases, BigQuery for analytics, VPC networking with firewall rules and load balancing, IAM roles and service accounts, Cloud Monitoring and Logging, and billing budgets. Google's documentation overview introduces these building blocks,¹ and its cloud computing primer explains the on-demand model.²

Google Cloud is used by 24.6% of developers in Stack Overflow's 2025 survey,³ and is especially common in data, analytics and AI teams. In India, AmbitionBox lists GCP engineers at a typical ₹10.7–12.4 lakh from a small sample⁴ and GCP data engineers at ₹9.8–10.8 lakh from more than 2,700 salaries.⁵

Developers using Google Cloud (Stack Overflow 2025)

24.6%

Typical GCP engineer pay in India (AmbitionBox, small sample)

₹10.7–12.4 lakh

Typical GCP data engineer pay in India (AmbitionBox)

₹9.8–10.8 lakh

Hierarchy levels: organisation, folders, projects

3

Table: How Google Cloud skills are assessed (editorial summary)

Table: A Google Cloud analytics pipeline (illustrative)

Table: How Google Cloud developed (editorial summary)

• A project structure with IAM groups
• An app on Cloud Run
• A BigQuery dataset with partitioned tables
• Budgets and alerts configured
• Infrastructure as code
• A verified Google Cloud fundamentals certificate

Show results: "Migrated nightly reporting to BigQuery with partitioned tables; reports from four hours to minutes; Cloud Run API; budget alerts caught runaway query."

"Compute Engine vs Cloud Run vs GKE?"

VMs for full control; Cloud Run for simple stateless containers; GKE for complex container platforms.

"How do you control BigQuery costs?"

Partitioning and clustering, selecting only needed columns, previews, quotas and budgets.

"What's a service account?"

An identity for applications; grant it only needed roles and avoid exporting keys.

"How are projects used?"

As boundaries for resources, permissions and billing — often per app and environment.

"How do firewall rules work in a VPC?"

They allow or deny traffic based on direction, ports, protocols and targets.

• "How are projects and folders organised?" — governance.
• "Which data services are used?" — focus.
• "How is IAM managed?" — security.
• "How are BigQuery costs monitored?" — maturity.

Table: Google Cloud–related roles and typical pay in India (AmbitionBox, October 2026; indicative)

Table: Common Google Cloud tools (editorial guidance)

• Cloud admin / analyst — operations or data.
• Cloud engineer / data engineer — builds workloads.
• DevOps / platform engineer — automation and GKE.
• Cloud or data architect — design.
• Head of cloud or data platform — strategy.

1. Week 1: Hierarchy and IAM — Projects, roles, service accounts, budgets.
2. Week 2: Compute — Deploy a container to Cloud Run.
3. Week 3: Data — Load data into BigQuery; partition tables.
4. Week 4: Prove it — Write it up; take the Google Cloud Fundamentals Certification.

Related: Cloud Computing, Kubernetes and Data Engineering. Guides: cloud computing, Kubernetes and data engineering.

• Deployments — Cloud Run, GKE, VMs.
• Data jobs — BigQuery pipelines.
• Access — IAM requests.
• Monitoring — alerts and logs.
• Costs — budget reviews.
• Documentation — architecture.

"What's the difference between regional and multi-regional storage?"

Location and redundancy trade-offs affecting availability, latency and cost.

"How do you give an app access to Cloud Storage?"

A dedicated service account with only the needed storage role, using workload identity rather than keys.

Table: A first-90-days plan (editorial guidance)

Learn how projects, access and billing are organised. Make changes through code and reviews. By the third month, improve something measurable — query costs, unused roles or idle resources.

Google Cloud offers integrated AI services and GPU and TPU capacity, and many data teams build AI features close to their BigQuery data. That adds work in data governance, access control and cost management for AI workloads. AI assistants in Google Cloud tools help write SQL, infrastructure code and troubleshoot, but their output must be checked for correctness, permissions and cost. Engineers who pair data skills with careful AI governance are in demand.

Don't grant broad basic roles like Editor to people or service accounts; use predefined roles. Avoid downloading service account keys — use workload identity. Set budgets on every billing account. Check bytes processed before running large BigQuery queries. Don't open firewall rules to the whole internet. Delete test projects when finished. These habits prevent most security issues and cost surprises.

Show the project structure, an architecture diagram, IAM roles granted, cost estimates and BigQuery optimisation choices. Explain why you chose Cloud Run or GKE. Share mistakes and fixes — interviewers value honest learning.

"It's only for data teams." Many companies run full applications on it.

"Serverless means no costs to watch." Requests, queries and egress still cost money.

"Editor role is fine for learning." Learn least privilege from the start.

In Google Cloud, IAM policies attach roles to principals — users, groups or service accounts — on a resource, and permissions inherit down the hierarchy from organisation to folders to projects to individual resources. A role granted on a folder applies to every project inside it, so be careful with broad grants high up.

There are three kinds of roles. Basic roles — Owner, Editor and Viewer — are very broad and best avoided for most use. Predefined roles grant specific permissions for a service, such as the ability to read objects in Cloud Storage or run BigQuery jobs. Custom roles let you combine exact permissions when predefined roles don't fit. Granting predefined roles to groups rather than individuals makes access easier to manage and review.

Service accounts are identities for applications. Give each workload its own service account with only the roles it needs. Avoid downloading service account keys, which can leak and are hard to track; instead, use attached service accounts on Google Cloud resources and workload identity federation for workloads running elsewhere. The IAM recommender can highlight unused permissions so you can tighten access over time.

Audit logs record who did what, and organisation policies can enforce rules such as restricting resource locations or blocking public access to storage. Together these give a strong foundation for secure projects.

BigQuery's on-demand pricing charges by the amount of data a query scans, so query design directly affects cost. Partition large tables by date and cluster them by commonly filtered columns, select only the columns you need instead of using SELECT *, and preview tables rather than querying them to look at data. Set custom quotas and budgets, and check the estimated bytes processed before running large queries.

• Broad Editor roles.
• Exported service account keys.
• Unpartitioned BigQuery tables.
• No budgets.
• Manual setups.

Google Cloud concepts come from Google Cloud documentation; usage from Stack Overflow's 2025 survey; and India pay from AmbitionBox (read October 2026; small sample for GCP engineers). Google Cloud is a trademark of Google; Science of Apps' test is independent and not affiliated. The pipeline example, project guidance and history are editorial. Last reviewed October 2026.

• Project — the basic container for Google Cloud resources.
• Service account — an identity for applications.
• BigQuery — Google Cloud's serverless data warehouse.
• Cloud Run — managed container hosting.
• GKE — Google Kubernetes Engine.