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TL;DR
📚 Clean Code · Head First Design Patterns · Designing Data-Intensive Applications · Building Microservices · Designing Web APIs · System Design Interview Vol. 2 · Infrastructure Engineer’s Textbook · Software Engineering at Google · Easy Kubernetes for Developers · CUDA-based GPU Parallel Processing
If you can explain the “content” of five or more of these ten books, you’re already ready to start a conversation with our team.

Why Talk About Hiring Criteria Through a ‘Book List’?

We value problem-solving attitude and learning depth more than experience.
The experience of reading a book to the end, applying it to practice, and even explaining it to colleagues proves sustainable competency in itself.
That’s why we consider the following ten books as basic literacy for backend·infrastructure engineers.

1. 《Clean Code》

  • Author — Robert C. Martin
  • Key Keywords — Readability, Refactoring, Naming
  • Why We Value It
    1. Long-term Maintenance — Even startups quickly develop legacy code. Clean code reduces costs.
    2. Team Communication — Good naming and function division serve as documentation. Enables communication faster than words.
    3. Refactoring Habits — The habit of making small improvements with test code enhances service stability.

2. 《Head First Design Patterns (2nd)》

  • Author — Eric Freeman & Elisabeth Robson
  • Key Keywords — Object-oriented, SOLID, Reusability
  • Why We Value It
    1. Pattern Language — Conversations like “Should we switch to the strategy pattern?” become possible, speeding up collaboration.
    2. Extensible Design — When requirements grow, you can ‘extend’ code rather than ‘overhaul’ it.
    3. Visual Learning — Pictures and interactive examples lower the learning curve.

3. 《Designing Data-Intensive Applications》

  • Author — Martin Kleppmann
  • Key Keywords — Distributed Systems, CAP, Event Sourcing
  • Why We Value It
    1. Scale Perspective — Enables evidence-based decisions like “Should we add some read latency to improve consistency?”
    2. Data Pipelines — Understanding CDC·stream·batch boundary conditions.
    3. Trade-off Thinking — Scientifically finding the balance point between performance·stability·complexity.

4. 《Building Microservices (2nd)》

  • Author — Sam Newman
  • Key Keywords — Domain Decomposition, CI/CD, Observability
  • Why We Value It
    1. Domain-Driven Decomposition — Making evidence-based judgments on ‘when’ to split monoliths.
    2. Observability — Reducing failure recovery time through integrated logs·metrics·tracing.
    3. Team Topology — Developing the perspective to design organizational and service structures together.

5. 《Designing Web APIs》

  • Author — Brenda Jin, Saurabh Sahni & Amir Shevat
  • Key Keywords — REST, OpenAPI, DX
  • Why We Value It
    1. Contract-First — OpenAPI-based design allows all client teams to run simultaneously.
    2. Versioning Strategy — Exposing new features without breaking compatibility.
    3. DX — Accelerating partner onboarding through automated documentation·sandbox·example code.

6. 《System Design Interview Vol. 2》

  • Author — Alex Xu, Sahn Lam / Translator — Lee Byung-jun
  • Key Keywords — Large-scale System Design, Interviews, Trade-offs
  • Why We Value It
    1. Problem Decomposition — Quickly structuring requirements with flowcharts·diagrams.
    2. Trade-off Communication — Persuading CAP·PACELC choices through ‘words’.
    3. Practical Interview Sense — Training to immediately set priorities under constraints.

7. 《Infrastructure Engineer’s Textbook》

  • Author — Sano Yutaka / Translator — Kim Sung-jae
  • Key Keywords — Server, Network, Virtualization, Operations
  • Why We Value It
    1. Full-stack Infrastructure Understanding — Physical·virtual·cloud layers come into view at a glance.
    2. Operations Optimization — Systematic approach to incident response·root cause analysis (RCA).
    3. MSP Perspective — Developing multi-tenant·SLA design sensibility.

8. 《Software Engineering at Google》

  • Author — Titus Winters, Tom Manshreck, Hyrum Wright / Translator — 개앞맵시
  • Key Keywords — Large-scale Codebase, Review, Automation
  • Why We Value It
    1. Code Health — Learning principles and cases of ‘sustainable code’.
    2. Review Culture — Presenting methods for practicing consensus-based quality management.
    3. Engineering Process — Understanding the productivity tool philosophy that led from Borg → Kubernetes.

9. 《Easy Kubernetes for Developers》

  • Author — William Denniss / Translator — Lee Jun
  • Key Keywords — Kubernetes, Deployment, Scalability
  • Why We Value It
    1. Practical Guide — Kubernetes objects and YAML writing become ‘immediately’ familiar.
    2. Operations Automation — Building stable services with rolling updates·health checks·HPA.
    3. Cloud-Native Thinking — Naturally acquiring the concept of ‘immutable infrastructure’.

10. 《CUDA-based GPU Parallel Processing》

  • Author — Kim Deok-su
  • Key Keywords — CUDA, Parallel Programming, Optimization
  • Why We Value It
    1. Performance Sense — Experiencing memory coalescing·thread warps through hand-coding.
    2. AI Infrastructure — Directly resolving bottlenecks in large-scale model training·inference pipelines.
    3. GPU Architecture Understanding — Depth that digs down to SM·Tensor Core level becomes competitive advantage.

The People We’re Looking For

Must-Read Book Your Proficiency Practical Example
Clean Code ✅ / ❌ Experience proposing refactoring points in internal code reviews
Head First Design Patterns ✅ / ❌ PR records applying patterns like Strategy·Observer·Decorator
Designing Data-Intensive Apps ✅ / ❌ Kafka + CDC based pipeline design·operation
Building Microservices ✅ / ❌ Building deployment pipelines for 10+ services
Designing Web APIs ✅ / ❌ OpenAPI Spec based code generation·version management
System Design Interview Vol. 2 ✅ / ❌ Experience solving large-scale system design problems for interviews
Infrastructure Engineer’s Textbook ✅ / ❌ Leading on-premises → cloud migration
Software Engineering at Google ✅ / ❌ Promoting large-scale refactoring with code review·automation tools
Easy Kubernetes for Developers ✅ / ❌ Helm·GitOps based Kubernetes operations
CUDA-based GPU Parallel Processing ✅ / ❌ 2× acceleration of model inference with custom CUDA kernels
  • If you can confidently fill six or more boxes with ‘✅’ in the above table, please apply!
  • We’d love to hear about cases where you’ve read deeply, coded by hand, and explained to colleagues during the interview.

Application Method & Inquiries

  1. Please send your resume·portfolio·GitHub link to info@thakicloud.co.kr.
  2. If you have cases where you applied what you learned from books to practice, please attach them freely in any format.

If you’re a colleague who’s serious about learning, we always keep our doors open.
We hope the underlines in six or more books will tell us your story.
Let’s create better code and better services together!

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참고