Engineering with Empathy
Engineering with empathy, as I define it, means understanding that there is a human on the other end of every line of code we write.
Engineering with empathy, as I define it, means understanding that there is a human on the other end of every line of code we write. It is about acknowledging that every technical decision we make has human consequences and that the ultimate measure of the code we write is not only its technical elegance but also how well it serves the actual users.
Empathy in Engineering begins with two simple things
The first and most important step is to understand who your users are, rather than just what features they require or how we will build them. When you can connect with the end user or put yourself in their shoes, everything becomes easier and decisions are made objectively.
The next step is to understand the entire context in which any feature, no matter how small, exists. Once you understand the why, it becomes easier to figure out how.
Why does empathy matter in engineering?
Empathy is often thought of as a soft skill, secondary to the hard logic of engineering. But, in reality, empathy combined with an engineer's technical depth and expertise can perform magic! It improves our problem-solving skills, reduces unnecessary complexity, fosters collaboration, and makes our work more fulfilling.
Problem Solving
When we truly understand our users' frustrations, workflows, and mental models, we can stop solving theoretical problems and start tackling real ones. Without empathy, it is simple to design for an abstract "user" who only exists in the theory. It enables us to create solutions that are practical in nature. This shift not only improves the impact of our work, but it also saves time and effort on features that no one needs.
Reducing Technical Debt
Engineering with empathy often results in cleaner, more maintainable systems—even though it might seem counterintuitive at first. When we truly understand how users interact with our software over time, we design for adaptability. We resist the temptation to over-engineer speculative features or prematurely optimise for scale that may never come. At the same time, we ensure that critical user flows are built with care, avoiding under-engineering that could lead to friction. The result? A system that evolves gracefully instead of accumulating unnecessary technical debt.
My most recent example was when I was integrating a payment gateway. We started with Razorpay, but we knew we wanted to support global payments in the near future. Instead of hardcoding logic specific to a single provider, we designed the entire payment system around state machines and reusable methods. This approach was not only intended to make future integrations easier for the business; it also ensured that any developer working on the system (including my future self) would not be burdened with unnecessary complexity. That's when I realized that empathy-driven engineering is about making life easier for everyone who interacts with the system, not just the users.
Finding Meaning
I believe that knowing who we are developing for and why we are doing what we are doing is key to finding fulfillment in our work. It is one thing to say "I write code," but completely different to say "I solve this problem for people by writing code." It's really exciting to know that what I create is actually helping people solve an important problem for them. It reminds us that behind every API call, performance optimization, and bug fix, there is a user trying to accomplish something important to them. That perspective provides more satisfaction than simply shipping features; it transforms our work into something meaningful.
How to Engineer with Empathy
After a few years of learning, here are practices that have helped me engineer with empathy:
Diving into User Feedback
This is something I strongly believe in: digging deep into user feedback. Don't delegate understanding of your users to the product team. Make it your business to understand who uses your software, including the smallest feature you've created, and how they feel about it. This includes actively engaging with feedback channels, reading support tickets personally, and looking beyond feature requests to detect emotional cues in user feedback.
One of my daily practices is to review user feedback to better understand how people use the product. It has become a consistent source of motivation. We track the Developer Love Index (DLI) for one of the products we're developing in HackerRank's Developer Community team on a regular basis—a measure of how much users value the product and whether they'd pay for it. It provides an instant energy boost. When the DLI rises, it indicates that we are on the right track. When it drops, it's a clear signal to investigate further and figure out what's wrong.
This viewpoint is powerful: even if only one user benefits from what we're creating, the right speed and direction can spread the impact to countless others facing similar challenges.
Get rid of preconceived notions
When you've spent weeks developing and testing a feature, it's natural to believe you know exactly how users will interact with it. After all, you've become the most experienced user. However, real users frequently navigate software in unexpected ways; what appears intuitive to you may be confusing or frustrating to someone else.
One of the most effective ways to combat this bias is to observe how actual users interact with the system. Sessions replays have changed the game for me. Actual user interactions can reveal pain points that specs, test cases, and even direct feedback cannot. It highlights the various applications of a feature and bases each decision on data rather than assumptions.
While the first version of any feature is always built with assumptions and hypotheses, this is where iterations come in handy.
Embrace "What If" Thinking
This involves identifying potential frustrations before they become major problems. A good habit is to pause and ask the right questions, for example:
What if the bot gets stuck while generating a response?
What happens if a payment fails? Will they understand what to do next?
Thinking about failure cases and edge scenarios helps to create a more seamless user experience. Proactively handling errors with clear, human-friendly messages can mean the difference between a frustrated and a delighted user.
One of the things I recently did for one of the AI features we are developing was to make the error message more human-friendly so that, in the worst-case scenario, the user sees the message; this may reduce the frustration of an already disappointed user.
Remember the problem you are trying to solve
One of the most important guiding principles in any discussion—whether it's naming a variable, structuring code, or deciding on the next feature—is to keep the core problem in mind. It's easy to get lost in discussions about implementation details, abstract optimizations, or even personal preferences.
However, at the end of the day, every decision should relate back to the fundamental question.
"What is the actual problem we are solving?"
Always begin with the "why" before diving into the "how" of implementing something.
Once we start understanding these fundamentals, the fuzziness starts becoming clear. For example, we need to worry about the variable name because it will become easy for you and other developers to come back to it in the future. We are building a feature X because it solves the problem Y.
I've gradually come to believe that empathy is more than just a nice-to-have soft skill for engineers; it's an essential value added along with technical depth and competency. It's what separates code that functions from code that serves, systems that run from systems that enable humans. So the next time you sit down to write code, remember: there's a human on the other side, and your empathy might be as important as your algorithms.
What practices have helped you engineer with empathy? I'd love to hear your experiences.