Your Infrastructure Is Code—So Why Is It Written by Amateurs?
Terraform has become the de facto standard for Infrastructure as Code (IaC). It promises a world where your entire cloud environment—from VPCs and subnets to Kubernetes clusters and serverless functions—is defined in clear, version-controlled, and auditable code. This is a revolutionary leap forward from the days of manual "click-ops" in a web console.
But this power comes with a terrifyingly sharp edge. When your Terraform code is written by engineers who lack a deep understanding of its state management, module architecture, and execution lifecycle, you are not building a resilient, automated platform. You are building a complex, opaque, and brittle time bomb that is waiting to detonate during your next `terraform apply`.
An engineer who can write a basic `.tf` file to provision an S3 bucket is not a Terraform expert. A Terraform expert understands the profound implications of state drift. They can structure a complex environment using a hierarchy of reusable modules. They can write a CI/CD pipeline that safely plans and applies changes to a production environment. They treat their infrastructure code with the same rigor and discipline as they would a critical backend service. These are the competencies that determine whether your IaC initiative is a strategic accelerator or a high-risk, high-cost operational nightmare.
Traditional Vetting and Vendor Limitations
A nearshore vendor sees "Terraform" on a résumé and immediately qualifies the candidate as a senior DevOps engineer. The interview might involve asking the candidate to define "state file" or to write a simple resource block. This process finds developers who have completed a "Terraform 101" tutorial. It utterly fails to find engineers who have had to recover a corrupted state file, refactor a monolithic root module, or manage a high-stakes production deployment with multiple dependencies.
The predictable and painful results of this superficial vetting become horribly apparent across your engineering organization:
- The Monolithic Root Module: Your entire production environment—across all services and all regions—is defined in a single, 10,000-line `main.tf` file. A single `terraform apply` takes 45 minutes to run and proposes changes to hundreds of resources, making it a terrifying, all-or-nothing operation that everyone is afraid to approve.
- State File Chaos: Multiple developers are running `terraform apply` from their laptops, resulting in a constantly drifting and often corrupted remote state file. Manual changes made in the AWS or GCP console have no corresponding code, and the true state of the infrastructure is a mystery.
- Copy-Paste Engineering: Instead of creating reusable modules, developers copy and paste the same 50 lines of code to create a new S3 bucket or database instance, but with slight variations each time. There is no consistency, and a single security policy change requires updating dozens of different files.
- "Plan" and "Apply" Theater: The team runs `terraform plan` in a pull request, but the plan is against a stale, out-of-date state file. The actual `apply` that runs in the CI/CD pipeline produces a completely different set of changes, leading to unexpected and often destructive outcomes.
The business impact is a toxic combination of operational risk, stalled velocity, and security vulnerabilities. You have adopted the most powerful IaC tool on the market and are using it to create a less reliable and more dangerous version of the manual process it was supposed to replace.
How Axiom Cortex Evaluates Terraform Developers
Axiom Cortex is designed to find the engineers who have internalized the principles of professional software engineering and apply them to infrastructure. We test for the practical skills and the safety-first mindset that are essential for managing production infrastructure with Terraform. We evaluate candidates across four critical dimensions.
Dimension 1: Terraform Code and Module Architecture
Well-structured Terraform code is the foundation of a scalable and maintainable IaC practice. A developer who writes monolithic, untestable code is creating a future maintenance nightmare. This dimension tests a candidate's ability to write clean, modular, and reusable Terraform code.
We provide candidates with a set of requirements for a new service and a poorly structured, monolithic Terraform project. We ask them to refactor it. We evaluate their ability to:
- Create Reusable Modules: A high-scoring candidate will immediately identify opportunities to extract repeated blocks of code into a dedicated, reusable module (e.g., a module for creating a standard, secure S3 bucket or a standard ECS service). They will understand how to define input variables and output values to create a clear module interface.
- Structure a Project for Multiple Environments: Can they design a directory structure that allows for managing multiple environments (dev, staging, prod) with shared modules but separate state files and variable definitions (e.g., using Terragrunt or native workspaces)?
- Use Expressions and Functions Effectively: Do they use Terraform's built-in functions, loops (`for_each`), and conditional expressions to write dynamic and concise code, avoiding unnecessary repetition?
- Manage Dependencies: Can they correctly use `depends_on` and module outputs to manage explicit and implicit dependencies between resources, ensuring that infrastructure is created and destroyed in the correct order?
Dimension 2: State Management and Execution Safety
The Terraform state file is the heart of the system. A developer who is careless with state management is a profound liability. This dimension tests a candidate's understanding of the Terraform lifecycle and their discipline in managing state safely.
We present a scenario and evaluate if the candidate can:
- Explain Remote State and Locking: Can they clearly articulate why running Terraform with a local state file is a bad idea for a team? Can they explain how remote state backends (like S3 with DynamoDB for locking) prevent concurrent modification and state corruption?
- Recover from State Drift: We describe a scenario where a resource has been changed manually in the cloud console, causing "drift." How would they bring the state file back in sync with reality? They should be able to discuss the pros and cons of using `terraform import` vs. `terraform refresh` vs. overwriting the resource.
- Handle Sensitive Data: How would they manage secrets (like database passwords) in Terraform? A high-scoring candidate will immediately talk about using a secrets management tool (like HashiCorp Vault or AWS Secrets Manager) via a data source, and why you should never store secrets in plain text in a `.tfvars` file or the state file.
Dimension 3: CI/CD and Automated Workflows
In a professional environment, `terraform apply` is not a command you run from your laptop. It is the final step in an automated, peer-reviewed pipeline. This dimension tests a candidate's ability to build a safe and reliable CI/CD workflow for Terraform.
We evaluate their ability to design a pipeline that:
- Validates and Formats Code: The pipeline should automatically run `terraform fmt` and `terraform validate` on every commit.
- Generates a Plan on Pull Requests: When a developer opens a pull request, the pipeline should run `terraform plan` and post the output as a comment, allowing for peer review of the proposed changes.
- Applies Changes with Manual Approval: The `terraform apply` step should only run on the main branch and should be gated by a manual approval step, ensuring that a human reviews the final plan before it is executed against the production environment.
- Handles Provider and Module Versioning: Do they understand the importance of pinning provider and module versions to avoid unexpected changes? How would they manage the process of upgrading versions safely?
Dimension 4: High-Stakes Communication and Problem Solving
Infrastructure is a high-stakes game. An elite infrastructure engineer must be able to communicate clearly about risk, diagnose complex problems, and collaborate effectively under pressure.
Axiom Cortex simulates real-world challenges to see how a candidate:
- Diagnoses a Failed `apply`: We give them the output of a failed Terraform run. We observe their diagnostic process. Do they read the error message carefully? Can they form a hypothesis about the root cause (e.g., a permissions issue, a resource conflict, an API rate limit)?
- Explains Risk to a Non-Expert: Can they explain to a product manager why a seemingly simple change has a large blast radius and requires careful testing? They must be able to articulate risk in business terms.
- Conducts a Thorough Code Review: When reviewing a teammate's Terraform code, do they look beyond syntax? Do they spot potential security misconfigurations, performance anti-patterns, or opportunities to use a more robust design?
From Brittle Scripts to a Resilient Platform
When you staff your infrastructure team with engineers who have passed the Terraform Axiom Cortex assessment, you are making a strategic investment in the stability, security, and velocity of your entire engineering organization.
A fast-growing fintech client was paralyzed by their infrastructure. Their small platform team was a bottleneck for every other product team. Provisioning a new database took weeks of manual work and back-and-forth communication. Using the Nearshore IT Co-Pilot, we assembled a "Cloud Foundation" pod of three elite nearshore Terraform engineers who had all scored in the 98th percentile on the Axiom Cortex assessment.
This team's mission was to build a self-service infrastructure platform. In their first six months, they:
- Created a Library of Standardized Modules: They built a versioned, private Terraform module registry with well-documented modules for all common infrastructure components (e.g., databases, caches, ECS services).
- Built a "Paved Road" CI/CD Pipeline: They created a standardized CI/CD pipeline that product teams could use to safely and automatically provision their own infrastructure using the shared modules.
- Codified and Enforced Security Policies: They used tools like Sentinel (in Terraform Enterprise) or OPA to write and automatically enforce security and compliance policies (e.g., "all S3 buckets must have encryption enabled") within the CI/CD pipeline.
The result was a revolution in developer productivity. The time for a product team to provision a complete new environment went from three weeks to under 30 minutes. The number of security misconfigurations in production dropped by over 95%. The platform team was freed from tedious, manual tasks and could focus on higher-level strategic work.
What This Changes for CTOs and CIOs
Using Axiom Cortex to hire for Terraform competency is not about finding someone who knows an IaC tool. It is about insourcing the discipline of software engineering and applying it to your infrastructure. It is a strategic move to reduce operational risk and increase engineering leverage.
It allows you to change the conversation with your CEO and your board. Instead of talking about infrastructure as a cost center, you can talk about it as an automated, self-service platform. You can say:
"We have built an infrastructure platform with a nearshore team that has been scientifically vetted for their ability to manage Infrastructure as Code at scale. This is not just making our operations team more efficient; it is providing a force multiplier for our entire product development organization, allowing them to ship features faster and more safely while ensuring we remain secure and compliant."
This is how you turn your infrastructure from a bottleneck into a competitive advantage.