If someone had told me a year ago that I would spend my summer working in a materials manufacturing company, I probably would have laughed. My background could not have been more different.
I completed my bachelor’s degree in information technology, worked as a data engineer for over three years, and am currently pursuing a master’s degree in computer science with a focus on machine learning and artificial intelligence. If there is one thing that excites me, it is data. Whether it’s designing databases, building data pipelines, creating dashboards, training machine learning models, or simply figuring out how to make data more useful, I genuinely enjoy every part of it.
So, when I accepted my internship at Indium Corporation, I had one big question.
What exactly does a data person do in a manufacturing company?
I assumed I would spend the summer designing a database, writing some SQL queries, and calling it a day.
I was completely wrong.
Within my first few weeks, I realized that software engineering and manufacturing are much more connected than I had ever imagined. In fact, I discovered that modern manufacturing runs on data just as much as it runs on machines.
Every manufacturing process generates an enormous amount of information. Sensors continuously collect readings, machines record operating conditions, laboratory tests produce measurements, operators enter production data, and engineers monitor countless process parameters. Before joining Indium Corporation, I had never stopped to think about where all of that information goes or how valuable it could be.
For someone who loves working with data, it felt like walking into a giant playground.
One of the first things I learned was that collecting data is only the beginning. The real challenge is deciding how to organize it so that it remains useful months, or even years, later.
As I dug deeper into the project, I found myself thinking beyond tables and schemas. How could data from different systems be brought together? How could massive volumes of data be processed efficiently? More importantly, how could that information be delivered to engineers in a way that was both timely and meaningful? These were the kinds of problems that made the work so engaging.
These are all software engineering problems, and solving them can have a direct impact on how efficiently a manufacturing process operates.
Another surprise was the sheer importance of data pipelines.
As software developers, we often think about APIs moving information between applications. In manufacturing, data pipelines connect machines, historians, laboratory systems, databases, reporting tools, and engineers. Building efficient workflows means that information reaches the right people quickly, allowing them to make informed decisions without waiting for manual reports or spreadsheet updates.
One thing I quickly realized was that efficiency is not just about writing better code. A well-designed data pipeline can significantly reduce the time engineers spend searching for information, manually compiling reports, or waiting for data to be processed. Saving just a few minutes in the flow of data can translate into hours of engineering effort saved over the course of a project, allowing engineers to focus more on solving problems than gathering information.
That realization naturally led me to another area where I found endless opportunities: business analytics.
Manufacturing companies generate incredible amounts of information every day. Some of it lives in databases, some in Excel spreadsheets, and some even in handwritten notebooks. Individually, those pieces of information may not seem particularly valuable. However, when they are combined, cleaned, and analyzed, they begin telling a story.
For example, we can calculate how long a machine operates before stopping, identify its average downtime, monitor production trends, or discover recurring process bottlenecks. These insights help engineers understand whether systems are performing as expected and where improvements can be made.
That is the exciting part about analytics: it transforms raw numbers into decisions. As someone studying artificial intelligence and machine learning, I naturally began wondering about predictive analytics. Manufacturing processes involve hundreds of variables. Temperature, pressure, chemical concentrations, timing, flow rates, and many other parameters influence the final product.
As someone pursuing a master’s degree in machine learning and artificial intelligence, I naturally started thinking about how these technologies could be applied to manufacturing. Every process generates a wealth of historical data, making it possible to train models that identify patterns humans might overlook. Imagine being able to predict how changes in temperature, pressure, or chemical concentrations might affect the final product before making adjustments on the production floor. AI is not there to replace engineers, but to provide another layer of insight that helps them make better, data-driven decisions. Realizing that made me appreciate just how much potential AI has within the manufacturing industry.
While AI may not replace engineers, it can become an incredibly powerful decision support tool, helping them identify patterns that would otherwise take months to discover.
Another area that fascinated me was the Industrial Internet of Things, often called the Industrial IoT. Modern manufacturing equipment is becoming increasingly connected through sensors, programmable logic controllers, and industrial networks. Every second, these devices generate valuable information about machine health and process performance. For software developers who enjoy embedded systems, networking, automation, or real-time systems, manufacturing offers an incredible opportunity to build solutions that directly interact with the physical world.
What fascinated me most was how closely software and the physical world are connected in manufacturing. Unlike many traditional software applications, where success is measured by what appears on a screen, the solutions you build here can improve actual production processes. Better software can help reduce downtime, optimize workflows, improve product quality, and make everyday tasks easier for the people who rely on these systems. Knowing that your work has a tangible impact makes the experience especially fulfilling.
One lesson I did not expect to learn was that software developers and manufacturing engineers approach problems in very similar ways. Engineers ask questions like, “Why did this process fail?” or, “How can we improve the yield?” Software developers ask, “Why did this program fail?” or, “How can we optimize this algorithm?”
Both professions rely on observation, experimentation, problem solving, and continuous improvement. The tools may be different, but the mindset is remarkably similar.
As Industry 4.0 continues transforming manufacturing, I believe the opportunities for software developers will only continue to grow. Whether it is databases, cloud computing, analytics, artificial intelligence, Industrial IoT, automation, cybersecurity, or digital twins, software is becoming an essential part of modern manufacturing.
My internship completely changed the way I view this industry.
I arrived expecting to build databases.
Instead, I discovered an entire ecosystem where software engineering plays a role in nearly every stage of the manufacturing process.
For students or software professionals who have never considered a career in manufacturing, my advice is simple: do not overlook it.
Behind every successful manufacturing process is an enormous amount of data waiting to be understood, optimized, and transformed into better decisions.
For someone who loves solving problems with technology, that sounds like the perfect place to be.
