Strong Understanding of SDLC and Software Engineering Best Practices – Why it matters more than ever when applying for engineering roles

In the past few years, the hiring environment in tech has changed significantly. 

Not long ago, many engineers experienced a market where companies competed heavily for talent and interview processes were relatively straightforward. Today, the situation looks different. Hiring has slowed in many sectors, the number of applicants per role has increased, and interview processes have become more demanding and structured. 

Because of that shift, companies are paying closer attention to fundamentals. One phrase that appears repeatedly in job descriptions is “strong understanding of the Software Development Life Cycle (SDLC) and software engineering best practices.” 

For many candidates, this requirement sounds familiar, yet it is often underestimated. 

With the rise of AI-assisted development tools, writing or improving code has become easier in many cases. Tools can help generate code, refactor existing implementations, and accelerate parts of the development process. What has not become easier is understanding how software moves from idea to production and continues to operate reliably after release. 

In other words, tools can assist with writing code, but they cannot replace an engineer’s understanding of how systems are designed, tested, delivered, and maintained within a real team environment. 

Understanding SDLC is therefore not just theoretical knowledge. It is a signal that a candidate can operate effectively within a structured engineering environment. 

What SDLC knowledge and understanding actually means 

The Software Development Life Cycle describes the structured process through which software moves from idea to production and ongoing maintenance. While specific frameworks may differ between companies, the core stages are usually similar. 

A strong candidate should understand how engineering work progresses through stages such as: 

• Requirements definition– understanding business problems, translating them into technical requirements, and clarifying scope before coding begins
• System and architecture design – choosing appropriate architectures, defining system components, and considering scalability, reliability, and maintainability
• Implementation – writing clean, maintainable code aligned with established engineering standards
• Testing and quality assurance – ensuring the system works as intended through automated tests, integration tests, and validation processes
• Deployment – releasing software safely through CI/CD pipelines and release management practices
• Maintenance and iteration – monitoring systems, fixing defects, and continuously improving the product 

Candidates who understand these phases demonstrate that they can think beyond their own code and consider the lifecycle of the entire system. 

However, the mindset they demonstrate during interviews is just as important. 

Strong candidates can clearly explain how a feature moves from idea to development, release, and monitoring. They describe their involvement across that process and show awareness of how their work fits into the broader delivery cycle. 

Candidates who lack this understanding often struggle to explain what happens after they commit their code or how deployments actually work in their team environment.

Software engineering best practices companies expect 

Alongside SDLC knowledge, employers look for familiarity with practices that ensure engineering quality and collaboration across teams. 

These typically include: 

• Version control workflows such as Git branching strategies
• Code review practices that improve quality and share knowledge across teams
• Automated testing including unit, integration, and end-to-end testing
• CI/CD pipelines that automate build, test, and deployment processes
• Documentation and knowledge sharing to ensure systems remain maintainable
• Observability and monitoring to detect issues in production environments 

These practices exist for one reason: large-scale systems require discipline. Companies want engineers who understand why these practices exist, not just how to execute individual tasks. 

Strong candidates usually describe their involvement in refinement, planning, and design rather than simply “picking up tickets.” They explain how they collaborate with the team to shape solutions and maintain delivery quality. 

Weaker candidates tend to describe work in isolation. Their explanations often sound like “I just code my part” or “QA handles testing.” 

Strong candidates can also explain how they keep code maintainable and testable. They talk about code structure, naming conventions, testing strategies, and the role of code reviews in maintaining quality. 

Less prepared candidates often struggle to describe how they estimate work, plan implementation, or deal with uncertainty in requirements.

How interviewers actually test SDLC understanding

Interviewers rarely assess SDLC knowledge through theoretical questions. Instead, they explore it through practical, scenario-based discussions. 

For example, they may ask questions such as: 

• How do you approach breaking down a new feature before implementation?
• What testing strategy would you use for this component?
• How would you handle a failed deployment?
• How do you ensure code quality within a team? 

Candidates who focus only on coding solutions often struggle with these questions. Strong candidates demonstrate awareness of the broader engineering process and the collaboration required to deliver software reliably. 

Many of these topics are explored through situational questions, where candidates are expected to describe real experiences. These questions are often best answered using the STAR method (Situation, Task, Action, Result). 

This means preparation matters. 

The days of simply “going to an interview and showing what you know” are largely behind us. Today, candidates are expected to demonstrate concrete examples of how they applied their knowledge in real situations. 

Without preparation, even experienced engineers may struggle to recall strong examples during the interview. 

What candidates should prepare 

If you are preparing for software engineering interviews today, it is worth revisiting these fundamentals. 

Practical preparation may include: 

• Reflecting on how yourpreviousprojects followed the SDLC stages
• Understanding the engineering practices used by modern teams
• Being able to explain your role within a larger delivery process
• Demonstrating awareness of quality, testing, and deployment considerations 

Even experienced engineers sometimes realize that they have worked within these processes for years but have never explicitly articulated them. 

It is also useful to reflect on how you typically describe your work. 

If you recognize patterns such as: 

• Using detached phrasing like “PM gives me tasks” or “someone else deploys it”
• Dismissing process discussions (“I don’t care about Jira”, “DevOps handles that”)
• Not mentioning testing, code review, documentation, or CI/CD when describing delivery
• Blaming process gaps instead of explaining how issues are raised or resolved 

this may indicate that your understanding of the broader engineering process needs strengthening. 

By contrast, strong candidates naturally describe practices such as: 

• Maintainingcode quality through structure, naming conventions, reviews, and testing
• Using CI/CD pipelines and validation environments before releases
• Raising risks or blockers early and ensuring transparency within the team
• Connecting technical decisions with delivery outcomes
• Actively giving and receiving feedback through code reviews 

The current hiring environment has placed renewed focus on engineering discipline. Companies are looking for professionals who understand not only how to write code, but how software is built and maintained in complex team environments. 

For candidates, demonstrating a strong understanding of SDLC and engineering best practices is therefore more than a checkbox in a job description. 

It signals that a candidate is ready to contribute to stable, scalable, and well-managed engineering systems from day one.