2026-02-05 11:09:47

Background

In early 2026, a small engineering team operating several self-hosted services began reassessing their external attack surface after a series of high-profile NAS and self-hosted platform breaches circulated in the security community.

The team was not running a large SaaS platform. Their environment was typical of many security-conscious developers and researchers:

• A self-hosted NAS exposed to the internet for remote access
• Multiple web-based management panels and internal tools
• Reverse proxy-based access (no direct port exposure to backend services)
• Strong passwords, HTTPS, and basic firewall rules already in place

Despite these controls, recent zero-day exploits involving path traversal and command injection—capable of bypassing authentication entirely—raised a familiar concern:

“What happens when the vulnerability is unknown, unpatched, and already being exploited?”

This question led them to deploy SafeLine WAF as an additional compensating control.

Threat Model: Why Traditional Controls Were Not Enough

From a defensive perspective, the team identified several uncomfortable truths:

• Zero-day web vulnerabilities often bypass:

  • Authentication mechanisms
  • Strong credential policies
  • Network-layer firewalls (because traffic is valid HTTP/S)

• Many NAS and self-hosted platforms:

  • Expose complex web interfaces
  • Contain legacy code paths
  • Cannot be patched instantly across all deployments

• Once exploited, attackers typically:

  • Read arbitrary files (credentials, backups, keys)
  • Execute system-level commands
  • Deploy persistence mechanisms

The team concluded that network-level and credential-based defenses alone were insufficient against modern web exploitation chains.

Defensive Strategy: Introducing a Reverse-Proxy WAF Layer

Rather than modifying each backend service individually, the team chose to insert a dedicated Web Application Firewall in front of all externally accessible web services.

Key selection criteria included:

• Transparent reverse-proxy deployment
• Coverage for common exploit classes (RCE, traversal, injection)
• Low operational overhead
• Self-hosted control (no dependency on cloud inspection)

SafeLine WAF was selected due to its explicit focus on application-layer attack detection and ease of integration in containerized environments.

Deployment Overview

SafeLine was deployed as a reverse proxy in front of the NAS web interface and other exposed services.

High-level architecture:

Internet
↓
SafeLine WAF (Reverse Proxy)
↓
NAS Web Services / Internal Applications

The deployment was completed using Docker Compose, allowing:

• Minimal changes to existing services
• Fast rollback if needed
• Centralized inspection of all inbound HTTP traffic

Within minutes, SafeLine began logging and classifying incoming requests.

Observed Attacks and Mitigation Results

Shortly after deployment, the team simulated known exploit patterns associated with recent NAS zero-day disclosures, including:

• Path traversal attempts (../, encoded variants)
• Command injection payloads in query parameters
• Suspicious request sequences targeting administrative endpoints

Results

SafeLine successfully:

• Detected and blocked traversal attempts before reaching backend services
• Identified injection payloads even when obfuscated
• Prevented malicious requests from triggering application-level execution

Crucially, these blocks occurred without relying on vulnerability-specific signatures, making them effective even when exact exploit details were unknown.

From the team’s assessment:

“Even if the backend were vulnerable, the payloads never made it past the WAF.”

Why This Matters for Zero-Day Defense

SafeLine did not “patch” the vulnerability. Instead, it acted as a virtual patch by enforcing strict application-layer behavior:

• Requests deviating from expected patterns were rejected
• Dangerous input structures were intercepted
• Exploit chains were broken before execution

This approach aligns with a widely accepted security principle:

When you can’t patch immediately, reduce exploitability.

Operational Considerations

From an operational security standpoint, the team noted several advantages:

• No changes to application code
• Clear visibility into attack attempts
• Ability to tighten or relax rules as needed
• Reduced reliance on constant emergency patch cycles

They also acknowledged that a WAF is not a replacement for patching, but rather a critical buffer during high-risk windows.

Lessons Learned

From this deployment, the team drew several conclusions relevant to the cybersecurity community:

1. Zero-day exploitation is now routine, not exceptional
2. Internet-facing management panels are high-value targets
3. WAFs remain one of the most effective compensating controls
4. Reverse-proxy WAFs provide strong protection with minimal disruption

For teams running self-hosted infrastructure, especially NAS platforms and internal tools exposed to the internet, adding an application-layer defense significantly reduces real-world risk.

2026-02-05 11:05:21

HTTPS doesn’t stop caching. It stops eavesdropping.

Your private API responses can still be cached by browsers, mobile apps, proxies, or CDNs.
If they contain tokens, PII, or account data — that’s ghost data left behind.

Rentgen checks authenticated endpoints and fails hard if caching isn’t explicitly disabled (no-store, private).
Not a warning. A real fail — because the impact is boring, common, and painful: data after logout, back button leaks, cached private responses.

This isn’t optimization.
It’s baseline security people forget because nothing breaks.

👉 Full story: https://rentgen.io/api-stories/cache-control-for-private-api.html

2026-02-05 10:54:07

Abstract

The rapid adoption of serverless computing, particularly AWS Lambda, has revolutionized application development, offering unparalleled scalability and cost efficiency. However, this architectural shift introduces new attack surfaces, often overlooked due to a shared misunderstanding of security responsibilities. This article explores critical misconfigurations in serverless functions, detailing how seemingly minor oversights can lead to severe security vulnerabilities like data exfiltration, privilege escalation, and supply chain compromises. My research aims to equip security professionals with a deeper understanding of these risks and practical strategies for robust defense, contributing valuable insights to the evolving landscape of cloud security. 🛡️

Research Context

Serverless architecture is no longer a niche technology; it is a cornerstone of modern cloud-native applications. Developers are drawn to its abstraction of infrastructure management, allowing them to focus purely on business logic. This paradigm shift, while beneficial for agility, redefines traditional security boundaries. The "shared responsibility model" in the cloud context means that while the cloud provider secures the underlying infrastructure, the user is responsible for the security in the cloud, including application code, data, and configurations. In serverless, this user responsibility often extends to the intricate permissions and environmental settings of individual functions, which are frequently misunderstood or misapplied, setting the stage for compromise. 🌐

Problem Statement

The core security gap in many serverless deployments lies not in the inherent security of the cloud platform itself, but in the misconfiguration of serverless functions and their associated resources. Developers, often prioritizing speed and functionality, may grant overly permissive IAM roles to Lambda functions, expose sensitive data through environment variables, or fail to manage third-party dependencies adequately. These misconfigurations create a stealthy attack vector, allowing attackers to exploit vulnerabilities that are unique to serverless architectures, often bypassing traditional perimeter defenses designed for monolithic applications. The ephemeral nature and distributed components of serverless make these issues harder to detect and remediate, leading to persistent security blind spots. 👻

Methodology or Investigation Process

My investigation into serverless misconfigurations involved setting up a simulated AWS environment, intentionally misconfiguring various Lambda functions and their integrated services. The process followed these key steps:

1. Environment Setup: Deployed several AWS Lambda functions using different runtimes (Python, Node.js) and integrated them with S3, DynamoDB, and API Gateway.
2. Configuration Analysis: Used open-source tools like Prowler and ScoutSuite to baseline security postures and identify common misconfiguration patterns across IAM roles, network settings, and data handling.
3. Vulnerability Simulation: Manually crafted and executed proof-of-concept attacks targeting specific misconfigurations. This included attempts at:
   • Role assumption and privilege escalation via overly permissive IAM roles.
   • Data exfiltration from S3 buckets using compromised Lambda function permissions.
   • Extraction of sensitive environment variables.
   • Injection of malicious code via vulnerable third-party dependencies within the Lambda package.
4. Runtime Observation: Monitored function invocation logs, CloudTrail events, and VPC flow logs to understand the footprint of successful and failed attacks.
5. Data Collection: Documented specific misconfiguration scenarios, the attack paths enabled, and the resulting impact. 📝

Findings and Technical Analysis

My investigation revealed several recurring and critical misconfiguration patterns:

1. Overly Permissive IAM Roles: Functions were often granted broad permissions like s3:*, dynamodb:*, or even ec2:* when only specific actions (e.g., s3:GetObject) were required. If an attacker gains code execution within such a function, they can leverage these excessive permissions to access or modify unrelated resources, effectively achieving privilege escalation within the cloud environment. Imagine a simple thumbnail-generator Lambda with s3:PutObject permission on one specific bucket, but actually having s3:* on all buckets. A code injection could then allow an attacker to delete critical data from an entirely different bucket. 📉

2. Insecure Environment Variables: Sensitive data, such as API keys, database credentials, or private access tokens, were frequently stored directly as Lambda environment variables. While AWS encrypts these at rest, they are readily accessible at runtime. A remote code execution (RCE) vulnerability, even a minor command injection, could allow an attacker to dump these variables and compromise downstream services or internal systems. 🔑

3. Vulnerable Third-Party Dependencies: Many Lambda functions utilized outdated or unpatched third-party libraries (e.g., npm packages, Python PyPI modules) with known CVEs. These vulnerabilities, often identified as part of software supply chain risks, could be exploited to achieve RCE within the Lambda function context. This grants attackers initial access, which they then use to exploit other misconfigurations. ⛓️

4. Lack of Network Segmentation and VPC Configuration: Functions handling sensitive data were sometimes deployed without proper VPC (Virtual Private Cloud) configurations, exposing them directly to the public internet or failing to restrict egress traffic. This eases data exfiltration for attackers and can simplify lateral movement if the function connects to internal resources without proper network controls. 📡

Risk and Impact Assessment

These technical flaws translate directly into significant real-world risks:

• Data Breach: Unauthorized access to databases, storage buckets, or internal APIs leading to exposure of sensitive customer data, intellectual property, or regulatory non-compliance.
• Privilege Escalation: An attacker gaining administrative access or control over critical cloud resources beyond the scope of the initially compromised function.
• Supply Chain Attacks: Compromise of development pipelines, leading to malicious code injection into serverless functions affecting downstream users or services.
• Resource Exhaustion and Denial of Service: Exploiting misconfigured functions to trigger excessive resource usage, incurring substantial cloud costs or making legitimate services unavailable.
• Reputational Damage: Loss of customer trust and brand damage stemming from security incidents. 💸

Mitigation and Defensive Strategies

Addressing these risks requires a multi-layered approach, emphasizing a "security-first" mindset in serverless development and operations:

1. Principle of Least Privilege: Strictly enforce the principle of least privilege for IAM roles assigned to Lambda functions. Grant only the absolute minimum permissions required for the function to operate. Utilize IAM Condition Keys for fine-grained control over resources and actions. 🤏
2. Secure Secret Management: Never store sensitive credentials directly in environment variables. Leverage AWS Secrets Manager or AWS Systems Manager Parameter Store for secure storage and retrieval of secrets, ensuring they are injected securely at runtime.
3. Dependency Scanning and Patch Management: Implement automated tools (e.g., Snyk, Dependabot, AWS Inspector) to continuously scan third-party libraries for known vulnerabilities. Establish a rigorous patch management process to update dependencies promptly.
4. VPC Configuration and Network Segmentation: Deploy sensitive Lambda functions within a VPC to restrict network access. Control inbound and outbound traffic using security groups and network ACLs, ensuring functions can only communicate with authorized endpoints.
5. Runtime Security and Monitoring: Implement runtime application self-protection (RASP) or similar solutions tailored for serverless environments. Monitor CloudWatch logs, CloudTrail events, and VPC Flow Logs for anomalous behavior and potential exploitation attempts.
6. Static Application Security Testing (SAST) and Dynamic Application Security Testing (DAST): Integrate SAST tools into the CI/CD pipeline to identify code-level vulnerabilities, and DAST tools to test deployed functions for runtime weaknesses.
7. Developer Security Training: Educate developers on secure coding practices, cloud security best practices, and the shared responsibility model. Foster a culture where security is integrated from the design phase. 🧑‍💻

Researcher Reflection

My journey through this serverless security research has been insightful. As Harsh, I've learned that the allure of developer velocity in serverless can often overshadow fundamental security considerations. The ease of deploying functions can lead to a 'set it and forget it' mentality, where initial secure configurations are not maintained as applications evolve. The most significant lesson for me has been the importance of shifting security left, integrating robust checks and balances throughout the development lifecycle, rather than trying to bolt them on as an afterthought. It's a constant battle to balance innovation with security, and honest self-assessment of our security posture is paramount. I can confirm this from my experience. 💡

Career and Research Implications

The findings from this research highlight a critical and growing demand for cybersecurity professionals with deep expertise in cloud and serverless security. Roles such as Cloud Security Engineer, Serverless Security Architect, and specialized Threat Hunter are becoming increasingly vital. For aspiring security researchers like me, this field offers immense opportunities for contributing novel detection techniques, automated remediation tools, and refined threat models specifically tailored for ephemeral, event-driven architectures. Ethical research and responsible disclosure remain foundational principles for advancing our collective security knowledge and protecting the digital landscape. 🎓

Call to Action

👉 Join the Cyber Sphere community here: https://forms.gle/xsLyYgHzMiYsp8zx6

Conclusion

Serverless computing, while transformative, is not inherently more secure than traditional architectures. The security posture of serverless applications hinges heavily on diligent configuration, continuous monitoring, and a profound understanding of the shared responsibility model. By proactively addressing misconfigurations in IAM roles, environment variables, dependencies, and network settings, organizations can significantly reduce their attack surface and build more resilient serverless applications. My research underscores that security in the cloud is a continuous journey requiring constant vigilance and adaptation. 🔒

Discussion Question

What novel detection or prevention strategies do you believe are most effective against zero-day misconfigurations in serverless environments, especially as functions become increasingly ephemeral and interconnected? 🕵️‍♀️

Written by Harsh Kanojia
https://harsh-hak.github.io/

2026-02-05 10:49:58

Building a Treasury SaaS with React, Node.js, Firebase & MySQL

Applying 25+ Years of Software Experience to a Modern Financial

Platform

Hello DEV community,

My name is Hernán Ricardo Ávila Castillo, a senior fullstack developer with more than 25 years of experience, currently based in Guatemala. I am also completing my degree in Computer Science and Systems Engineering.

At the moment, my main focus is the development of a Treasury Management SaaS platform: a modern system designed to support real-world financial operations, payment workflows, balances, and business controls.

In this post, I would like to share what I am building, why I selected this technology stack, and how I approach architecture when working on a fintech-oriented product.

The Project: A Treasury / ERP Ecosystem

The platform is being designed to support:

• Treasury operations
• Cash flow monitoring
• Accounts payable and receivable
• Payment approval workflows
• Financial tracking across projects and organizations
• A scalable SaaS foundation for future ERP modules

This is not a demo project. The goal is to evolve it into a production-grade enterprise platform.

Technology Stack

After working with many technologies throughout my career, I decided to build this treasury system using a modern and reliable stack:

Frontend: React

React provides an excellent foundation for:

• Admin dashboards
• Component-driven interfaces
• Long-term scalability in UI development

Backend: Node.js

Node.js enables:

• Fast API development
• Strong integration capabilities
• Clean service-oriented architecture

Authentication and Platform Services: Firebase

Firebase is used for:

• Secure authentication
• Notifications
• Realtime capabilities when needed
• Simplified scalability for SaaS workflows

Core Financial Database: MySQL

MySQL remains a strong choice for treasury data because it provides:

• Relational consistency
• Transactional reliability
• Strong reporting and structured modeling

High-Level Architecture

The platform follows a modular SaaS architecture:

React Admin Dashboard
        |
        v
Node.js REST API Layer
        |
        +-------------------+
        |                   |
   Firebase Auth       MySQL Database
        |                   |
 Notifications        Treasury Ledger + ERP Data

This separation allows:

• Clear API boundaries
• Enterprise-grade data integrity
• Independent scaling of modules

Key Design Principles

After more than two decades building software, several principles remain constant:

1. Financial systems require strong consistency

Treasury platforms must be auditable, traceable, and transaction-safe.

Data integrity is not optional.

2. SaaS requires modular thinking

Every module must evolve independently:

• Payments
• Clients
• Projects
• Approvals
• Reporting

3. Automation is part of the future

The platform is being developed with automation in mind, integrating AI-driven workflows using tools such as n8n and Make.

Treasury is not only accounting. It is operations.

What I Am Building Next

Upcoming milestones include:

• Multi-tenant SaaS structure
• Role-based access control
• Payment gateway integrations
• Approval pipelines
• Financial reporting dashboards
• Cloud-native deployment workflows

Why I’m Sharing This Here

I am joining DEV.to to:

• Share real-world software engineering experience
• Learn from the community
• Document the evolution of this treasury platform
• Connect with others building fintech and enterprise systems

Final Thoughts

Even after decades in software, building systems like this remains exciting. Technology evolves constantly, and so do we as engineers.

If you are working on treasury platforms, ERP systems, fintech SaaS products, or React/Node architectures, I would be glad to connect and exchange ideas.

Thank you for reading.

Links

• GitHub: https://github.com/hricardoavilac
• Website: https://racastillo.com

#dev #react #node #firebase #mysql #saas #fintech #softwarearchitecture

2026-02-05 10:43:56

I still remember my first interview with a major international tech company. I had practiced infrastructure design patterns for weeks, could explain most AWS services in my sleep, and had memorized every automation project I'd built. Then the interviewer asked a question that threw me completely: "Walk me through how you would handle a critical production outage affecting multiple regions while coordinating with teams across three time zones."

My mind went blank. In my previous roles at local companies, influence typically followed hierarchy. The concept of "influence without authority" wasn't just a challenge to answer—it was almost culturally foreign.

This is just one example of how interviewing for global DevOps and cloud architecture roles requires more than technical preparation. It demands an understanding of different workplace cultures, communication styles, and expectations that might not be obvious to engineers whose experience has been primarily local.

The good news? These differences are learnable. Once you understand the unwritten rules of global infrastructure interviews, you can showcase your true capabilities to international employers, regardless of where you're based.

Understanding the Global DevOps Interview Landscape

Most major global tech companies structure their DevOps/Cloud Architecture interviews with these components:

Initial screening (online tests or recruiter call)

Technical phone/video screens (1-2 cloud design or troubleshooting sessions)

Infrastructure design interview (architecting scalable, resilient systems)

Cultural fit interviews (assessing DevOps collaboration and mindset)

Hiring manager conversation (determining team and operational fit)

The weight varies by company: AWS and Google Cloud emphasize architecture design and automation, Netflix and Spotify focus heavily on reliability and incident response, while traditional enterprises often prioritize security and compliance alongside automation.

Cultural Differences in DevOps Philosophy

North American companies tend to value automation-first mindsets, "move fast and break things" culture, blameless post-mortems, and individual ownership of services.

European companies often emphasize thorough documentation, security-first approaches, work-life balance in on-call rotations, and systematic change management processes.

Understanding these cultural differences helps you frame your operational experience appropriately for different opportunities.

Common Misconceptions Holding African DevOps Engineers Back

Through mentoring dozens of African DevOps engineers, I've noticed these limiting beliefs:

"My infrastructure experience with limited resources isn't valuable." Reality: Building resilient systems with constraints demonstrates exactly the efficiency and resourcefulness global companies need.

"I should downplay my manual processes experience." Reality: Understanding both manual and automated approaches makes you a stronger automation engineer who knows what to prioritize.

"My on-premises experience isn't relevant to cloud roles." Reality: Deep infrastructure knowledge translates directly to cloud architecture, often making you more effective than cloud-only engineers.

Technical Preparation: Infrastructure and Automation Focus

Cloud Architecture Interviews: What They're Really Assessing
Beyond knowing cloud services, interviewers evaluate:

Infrastructure design thinking: Do you consider redundancy, scalability, and cost optimization? Can you design for multiple failure scenarios?

Automation mindset: How do you approach Infrastructure as Code? What's your strategy for CI/CD pipeline design?

Operational awareness: Do you consider monitoring, logging, and alerting in your designs? How do you handle security and compliance?

Cost and efficiency consciousness: Can you optimize for performance while managing costs? Do you understand resource right-sizing?

Effective preparation techniques:

Practice infrastructure diagramming: Use tools like draw.io to design architectures while explaining your choices
Mock architecture reviews: Present your designs to peers and defend your technology choices
Cost optimization scenarios: Practice explaining how you'd reduce infrastructure costs while maintaining performance
Infrastructure Design: Level-Appropriate Expectations
Mid-level DevOps engineers should demonstrate understanding of cloud fundamentals, ability to design automated deployment pipelines, knowledge of monitoring and logging strategies, and basic security and compliance awareness.

Senior DevOps/Cloud architects need experience designing enterprise-scale infrastructure, deep understanding of trade-offs between different cloud services, ability to design for compliance and security requirements, and expertise in cost optimization strategies.

Preparation strategy:

Master the "Big 3" cloud providers' core services (compute, storage, networking, databases)

Practice designing real-world scenarios: e-commerce platforms, content delivery networks, data processing pipelines

Develop systematic approaches: requirements gathering → architecture design → security considerations → cost optimization → monitoring strategy

Build expertise in specific areas: Kubernetes, serverless architectures, data engineering, or security

Automation and Tooling Discussions

Be prepared to discuss:

Infrastructure as Code: Terraform, CloudFormation
CI/CD pipelines: Jenkins, GitLab CI, GitHub Actions, Azure DevOps design and optimization
Configuration management: Ansible or Chef for server and application configuration
Container orchestration: Kubernetes, Docker, ECS/Fargate operational experience
Monitoring and observability: Prometheus, Grafana, ELK stack, DataDog implementation strategies
Mastering DevOps Behavioral Interviews
DevOps behavioral interviews focus on operational scenarios, incident response, and cross-team collaboration.

The DevOps-Adapted STAR Method

Situation: Describe the operational context—system scale, business criticality, team structure.

Task: Clarify your specific operational role and responsibilities, especially in incident response or automation projects.

Action: Highlight both technical actions and collaboration approaches. Emphasize how you balanced speed with stability, communicated with stakeholders, and improved processes.

Result: Quantify operational improvements (uptime, deployment frequency, recovery time). Include lessons learned and process improvements implemented.

DevOps-Specific Behavioral Topics

Prepare stories demonstrating:

Incident response leadership: Managing critical outages, coordinating teams, communication during crises
Automation impact: Replacing manual processes, reducing deployment times, improving reliability
Cross-team collaboration: Working with development teams, bridging dev and ops cultures
Scalability challenges: Handling traffic spikes, scaling infrastructure, capacity planning
Security integration: Implementing DevSecOps practices, compliance automation
Process improvement: Implementing monitoring, improving deployment processes, reducing toil
Learning from failures: Post-incident reviews, implementing preventive measures
Example strong response: "When our e-commerce platform experienced a 300% traffic spike during a flash sale, I led the incident response that included auto-scaling our container infrastructure, coordinating with the development team to identify a database bottleneck, and implementing a temporary caching layer that reduced database load by 70%. We maintained 99.9% uptime during the event and used the learnings to implement predictive scaling that prevented similar issues."

Communication and Practical Considerations

DevOps-Specific Communication Skills
For technical architecture discussions:

Explain trade-offs between different architectural approaches
Discuss operational implications of design decisions
Connect technical choices to business outcomes
During incident scenario discussions:

Demonstrate structured problem-solving approaches
Show clear communication strategies for stakeholders
Explain escalation procedures and team coordination
Discuss both immediate fixes and long-term preventive measures
Infrastructure cost discussions:

Present cost optimization strategies with concrete examples
Explain resource right-sizing approaches
Discuss reserved instances, spot instances, and other cost management techniques
Balance cost optimization with performance and reliability requirements
Managing Infrastructure Challenges
Proactive preparation for global companies:

Understand 24/7 on-call expectations and rotation strategies
Prepare for multi-region, multi-time-zone operational scenarios
Research company-specific operational tools and practices
Demonstrating operational maturity:

Discuss monitoring and alerting strategies that prevent incidents
Explain automation approaches that reduce manual intervention
Show experience with capacity planning and performance optimization
Demonstrate understanding of disaster recovery and business continuity
Research That Sets You Apart
Beyond company basics:

Study their infrastructure architecture (if publicly discussed)
Research their operational challenges and recent outages
Understand their cloud strategy and technology stack
Review their engineering blog posts about infrastructure and operations
Connect your experience to their operational needs:

Identify their scalability challenges: "I see you're expanding globally. I've designed multi-region failover systems that maintained sub-second failover times."
Address their automation maturity: "I noticed your focus on DevOps transformation. I've led similar initiatives that reduced deployment time from hours to minutes."
Your DevOps Success Framework
3-Week Preparation Plan
Week 1: Technical Foundation

Days 1-2: Assess cloud architecture knowledge, research company infrastructure stack
Days 3-5: Practice infrastructure design scenarios, review automation tools and best practices
Days 6-7: Study incident response frameworks and operational procedures
Week 2: Behavioral and Operational Scenarios

Days 8-9: Identify 5-7 operational stories covering incidents, automation, and collaboration
Days 10-11: Practice explaining technical architectures clearly, get feedback on communication
Days 12-14: Conduct mock interviews including architecture design and incident response scenarios
Week 3: Final Preparation

Days 15-16: Deep research into company's operational challenges and infrastructure strategy
Days 17-18: Technical setup for virtual whiteboarding, practice with drawing tools
Days 19-21: Mental preparation, review strongest operational examples, relaxation techniques
Interview Day Checklist:

Test whiteboarding tools and screen sharing 2 hours before
Have architecture diagramming tools ready (draw.io, Lucidchart, etc.)
Prepare to discuss specific technologies from their stack
Review your most impactful automation and operational improvement stories
Showcasing Your Unique Value as an African DevOps Engineer
The operational constraints you've navigated—limited resources, unreliable infrastructure, cost optimization pressures—have developed valuable skills:

Efficiency and resource optimization: Building more with less, understanding true cost implications
Resilience engineering: Designing systems that work despite infrastructure challenges
Creative automation solutions: Finding efficient ways to automate with limited tooling
Operational pragmatism: Balancing ideal practices with practical constraints
As Funmi, now a Principal Cloud Architect at a major fintech, shared: "I used to think my experience optimizing for expensive bandwidth and unreliable power was a limitation. But when I explained how I'd designed systems that gracefully degraded during infrastructure failures and optimized for minimal data transfer, they immediately saw the value for their cost optimization and resilience initiatives."

The Global DevOps Mindset
Approaching international DevOps interviews requires the right operational mindset:

Think like a global operator: Your infrastructure skills scale across any environment

Emphasize operational excellence: Focus on reliability, efficiency, and continuous improvement

Showcase collaborative leadership: Demonstrate ability to bridge teams and manage incidents

Balance innovation with stability: Show you can implement new technologies while maintaining operational excellence

The global technology landscape increasingly values operational expertise that can build resilient, efficient, and scalable infrastructure. African DevOps engineers often bring exactly this practical, efficiency-focused approach that global companies need.

Your next career breakthrough isn't just about the tools you know—it's about demonstrating how effectively you can design, automate, and operate infrastructure that enables global business success.

2026-02-05 10:30:00

Everything worked.

Which, in hindsight, was the problem.

I had just shipped a small backend change. The kind you barely think about. Tests passed. Local setup was green. I even did that extra manual check we all pretend to always do.

A few hours later, production started acting… weird.

Not broken. Not down. Just off.

Some requests failed. Others succeeded. Refresh the page and the result changed. At one point I honestly wondered if I was accidentally load testing my own sanity.

My first thought was data. Then traffic. Then timing. Then maybe I had angered the JavaScript gods.

I added logs. Lots of logs. The kind you swear you’ll remove later.

Nothing obvious showed up.

That’s when I noticed something small and extremely annoying. One configuration value was undefined in production but perfectly fine on my machine.

I stared at it longer than I’d like to admit.

I had assumed my environment variables were the same everywhere. They weren’t. Locally, I had an old config file quietly saving me. In production, that variable simply did not exist, and my code reacted to that fact with chaos.

Once I knew that, the fix was almost boring. Add validation. Set a default. Deploy again.

The real bug wasn’t the code. It was my assumption that environments behave politely and consistently.

Since then, whenever a bug feels random, I start by asking a simple question: what am I assuming is “obviously the same” when it probably isn’t?

It saves time. And a small amount of dignity.