15 Techniques I Rely On Daily as a Senior Software Engineer

Early in my career, I thought seniority was measured by lines of code or how many obscure languages I knew. I optimized for speed and cleverness. But over time, the focus shifted. Today, my job isn’t just about writing code — it’s about building systems that survive, scale, and stay maintainable long after I’ve moved on to the next ticket.

The “secret sauce” isn’t a specific framework or tool. It’s a set of habits and mental models. Here are the 10 techniques I use every single day to keep my systems sanity-checked and my code production-ready.

1. Writing Before Coding 🔗

This idea is also known as “Think First, Code Last”.

It’s tempting to dive straight into the IDE, but I’ve learned that the keyboard is the last place design happens. I always start with a document usually it’s a README.md, a set of comprehensive comments, or a rough architectural diagram.

Before writing a single line of code, I invest significant time analyzing the problem, understanding the business requirements, and planning the architecture or solution on paper or a design document. This prevents wasted time and ensures the right problem is solved.

The rule is simple: If I can’t explain it clearly in plain English, I’m not ready to code it. Writing forces you to structure your thoughts and exposes logical gaps that code often hides until it’s too late.

2. Reading Code More Than Writing It 🔗

We often romanticize the act of creation, but maintenance is where the real work happens. Most bugs, edge cases, and design flaws aren’t in the code you’re about to write — they are hiding in the code that already exists.

I treat reading code as a form of leverage. Deeply understanding the existing system — its quirks, its history, and its flow — prevents me from reinventing the wheel or breaking a critical dependency I didn’t know existed.

3. Thinking in Failure Modes 🔗

Junior engineers code for the “happy path” where the network never blips and the database is always instant. Senior engineers code for the apocalypse.

I constantly ask, “What breaks first?” I obsess over timeouts, retries, partial failures, and backpressure. If a third-party API goes down, does my whole app crash, or does it degrade gracefully? Thinking in failure modes turns fragile features into resilient systems.

4. Measuring Before Optimizing 🔗

Premature optimization is the root of all evil, but blind optimization is a close second. I never trust my intuition on where a bottleneck lies because intuition is usually wrong.

I lean heavily on logs, metrics, and traces. Intuition lies; numbers don’t. I only start refactoring for performance once I have hard data proving exactly which function or query is slowing us down.

5. Designing for Deletion 🔗

Code is liability, not asset. The best code is code you can easily throw away when requirements change.

I aim to make every abstraction easy to delete. If a module is so tightly coupled that removing it requires a week of surgery, it’s too heavy. Designing for deletion encourages loose coupling and modularity, making the inevitable pivot much less painful.

And this point is the most painful one!

6. Simplifying Hot Paths 🔗

The “hot path” — the code executed most frequently — is no place for showing off. In these critical sections, I stick to boring, explicit, and predictable code.

Clever one-liners and complex abstractions are pushed to the edges of the system. On the main highway, I want clarity. If there’s a bug in the hot path, I need to be able to spot it in seconds, not spend hours deciphering a “clever” algorithm.

And that’s partially why I tend to love Go language and use it more often.

7. Making Trade-offs Explicit 🔗

There is no such thing as a perfect solution, only trade-offs.

Latency vs. consistency.

Speed vs. safety.

Cost vs. complexity.

CPU cycles vs. RAM usage.

The mistake isn’t making a trade-off; it’s making it unconsciously. I state these decisions clearly in design docs and code comments so the whole team aligns. We aren’t just choosing a database; we are choosing eventual consistency over immediate consistency, and we all need to agree on why.

8. Writing Code for the Next Reader 🔗

I don’t write code for the compiler; I write it for the human who has to fix it six months from now. Often, that human is me.

This means clear variable names, small and focused functions, and obvious control flow. “Future Me” is my most important customer. If I have to burn “brain cycles” just to understand what a function does, I’ve failed.

9. Using Constraints Intentionally 🔗

Infinite resources are a myth. I use constraints to create stability. This means setting hard limits on memory usage, defining strict timeouts, and implementing rate limits from day one.

Unbounded systems eventually fall over. Constraints act as guardrails that keep the application running within safe parameters, preventing a runaway process from taking down the entire server.

10. Continuous Refactoring 🔗

The “Big Rewrite” is a trap. It rarely happens, and when it does, it usually fails. Instead, I practice continuous refactoring.

I make small improvements daily—cleaning up a variable name here, extracting a function there. It’s like sweeping the floor; if you do it every day, the house stays clean. If you wait a year, the mess becomes unmanageable, and the system begins to rot.

11.Prioritize Ruthlessly 🔗

I use a system (like “Now, Next, Later”) to focus on high-impact tasks that move the business goals forward, rather than getting sidetracked by every email or minor request.

12. Ask “Why” Often 🔗

I consistently question the assumptions behind new ideas or feature requests to uncover the true user need or business value. This helps simplify complex problems and avoid building unnecessary features.

13. Automate Repetitive Tasks 🔗

If I find myself doing the same manual task more than a couple of times, I automate it with scripts or tooling. This saves significant time in the long run and reduces the chance of manual errors.

14. Shorten Feedback Loops 🔗

I am a zealot for fast tests and continuous integration/continuous deployment (CI/CD) pipelines. Getting rapid feedback on code changes allows for quicker iteration and earlier bug detection.

As a founder of Kart Business myself, I love this as it makes perfect sense for software development and the business side as well.

15. avoid overengineering 🔗

I always try to avoid over-engineered solutions.

I write before coding, discuss the problem and its solutions. I draft a systematic solution that integrates well. I tend to use the simplest fix for the real underlying problem.

For example, I was trying to save the system log of the server to review it later if anything wrong happen. I started thinking of too many scenarios and solutions, but I finally settled on a simple thing like this:

go run . | tee server.log

The idea is to use tee utility that shows the output on the standard output but it saves it in the specified file as well. It’s so simple but elegant.

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