The Upstream World: Software Engineers and Product Team

Welcome to the second installment of “Data Engineering Demystified.”

In the first installment, we looked at why data engineering can sometimes feel like a “black box” to those outside it. We compared pipelines to plumbing, unseen when it’s working, but painfully obvious when it isn’t.

This time, we’re moving upstream to the world of software engineers and product teams, and exploring how their work shapes, challenges, and sometimes complicates the life of a data engineer.

Software engineers and product managers are often seen as the darlings of the tech world. Everyone knows what they do. They’re the builders and shippers of features, the makers of magic. Product managers act as mini-CEOs for those features, posting on LinkedIn about the latest release that just hit a million users.

That’s all fine and good. But upstream producers often have very real implications for their downstream consumers: the data engineers.

That’s us.

We take everything that software engineers create and everything product teams dream up and turn it into something measurable and meaningful. And here’s the rub: software teams aren’t always thinking about how their choices affect us downstream. Their goal is to release features fast, delight users, and make the experience sticky. Our goal is to make the resulting data usable, reliable, and ready for analysis.

Those goals don’t always align.

When the Data Model Bites Back

Most of the time, the end user never sees the problem. The app works, the features delight, and the dopamine hits keep coming. But behind the scenes, data engineers inherit whatever structure the software team happened to ship, and that structure can sometimes make life messy.

Take a mobile app like Duolingo. Every time the bird dances, data is being captured: user engagement, feature clicks, rewards earned. The product team may only see a smooth user experience. We data engineers see the raw data, and sometimes, the model behind it is a nightmare. Columns mislabeled. JSON files thousands of lines long. Nested fields inside nested fields. It works fine for the app, but it’s painful for analysis.

The issue isn’t bad intent; it’s lack of awareness. When software engineers are heads-down shipping features, they’re not asking, “How will this data be queried six months from now?” or “Will anyone be able to make sense of this schema?” But they should be thinking this way. Because the day will come when someone, usually a CEO, late on a Sunday night, sends a frantic email asking, “Did that new feature increase conversions or not?”

If the data model wasn’t designed with downstream use in mind, that answer can take days instead of minutes to deliver.

Lessons from the Older Sibling

Software engineering is the older sibling of data engineering.

They’ve been around longer, built more systems, and squashed more bugs. Many of the practices we data engineers now embrace (data contracts, quality checks, error handling, type enforcement) have long been standard in software development.

That’s great news. It means we can learn from them. But, as with many younger siblings, we don’t always enjoy the same respect. Everyone knows what a software engineer does. Fewer understand what a data engineer does, or why what we do is just as important.

Think of it this way: when you’re remodeling your kitchen, you might obsess over the sink, its size, style, and finish. You’re not thinking about the pipe fittings, sealants, or water pressure. But those details determine whether the sink will actually function properly. Software engineers build the sink. Data engineers make sure the water flows through it.

But the relationship doesn’t only flow in one direction.

Data engineering has matured by borrowing some of the same habits software teams have long practiced: version control, structured code reviews, and outcome-based testing. These disciplines help us keep data pipelines reliable, reproducible, and easier to debug.

At the same time, software engineers can benefit from thinking more like data engineers, by designing their applications with data quality, lineage, and usability in mind. That shared awareness helps both teams build stronger systems and avoid surprises downstream.

In other words, this isn’t about one discipline copying the other. It’s about meeting in the middle. When data teams adopt stronger engineering rigor, and software teams design with data in mind, the whole ecosystem functions better.

The Cost of Ignoring Downstream

When product and engineering teams don’t consider how data will be produced and consumed, small oversights can snowball into major inefficiencies. A data payload that’s “flexible” for developers can be a nightmare for analysts; deeply nested, inconsistent, or just plain unreadable.

Yes, it’s part of our job to clean, flatten, and transform messy data. But thoughtful design upstream saves everyone time downstream. When data quality starts at the source, pipelines flow smoother, dashboards refresh faster, and insights arrive on time.

That’s why collaboration is key. The best teams work as pods, not silos–product, software, and data engineers thinking together about how features will be measured, how schemas will evolve, and how the data will tell the story later.

Because when those conversations don’t happen, organizations end up flying blind. They ship features they can’t evaluate, run experiments they can’t measure, and lose momentum chasing answers that should have been easy.

The Big Takeaway

If you’re an upstream software engineer, product manager, or architect, think of your data colleagues as your partners, not an afterthought. Implement clean APIs, data quality checks at the source, and clear data contracts.

And if you’re a data engineer, lean into the engineering disciplines that your software peers have refined, namely testing, reproducibility, and design reviews. The bridge between the two worlds isn’t just technical; it’s cultural.

When you do this, you’re not just helping data engineers; you’re helping yourself. You’re making it possible for the organization to measure success, iterate faster, and make decisions based on truth over guesswork.

Data engineering doesn’t exist to slow you down, it exists to make sure all your hard work upstream actually pays off for the downstream data consumer.