Data engineer

Standing out as a data engineer

Introduction

Whether it’s a desire to stand out in a competitive job market, receive a promotion, or just personal sense of achievement, we all want to stand out as a data engineer that has that special something that makes them more effective than others.

Oftentimes in the pursuit of this goal, aspiring or junior engineers take a bunch of courses to become proficient in the hot new tools. They may read books on fundamentals of data engineering to better understand data modeling, ETL, or distributed processing.

While these can all be helpful tools, I have found that the best way to stand out as a data engineer is a singular focus on the business.

The basic role of a data engineer

A data engineer’s job is to create and maintain data systems that collect, store, and serve data.

However, in practice, this is not enough. Many engineers assume that their job is solely about pipelines and infrastructure, but those are just the means, not the end goal. The true purpose of a data engineer is to enable better decision-making and drive business impact through high-quality data.

The anti-pattern

What data engineers should avoid at all costs is being seen as just a tool builder. This is a data engineer who is focused on technology for technology’s sake, constantly tweaking their systems to incorporate the newest frameworks or architectures. This engineer executes whatever requirements they receive from stakeholders without questioning them. Once they get their requirements, they disappear for weeks, returning triumphantly with a newly constructed data pipeline. They hand it off and move on to the next project.

This may sound like a great engineer:
✅ They stay on top of the latest tools.
✅ They efficiently complete their assigned work.
✅ They are constantly trying to optimize their pipelines.

But this approach is deeply flawed.

What this leads to

The problem with the above approach is that it ignores the true purpose of the data engineer—to solve business problems. Business stakeholders are generally not experts in data. The requirements they provide may not accurately reflect what is most needed by the company.

When data engineers focus exclusively on execution, they:
❌ Waste time building unnecessary or low-impact pipelines.
❌ Fail to uncover hidden business needs that could drive greater value.
❌ Are seen as order takers rather than strategic partners.

This results in low influence, missed opportunities, and lack of career growth.

A shift to focusing on business outcomes

At the end of the day, the only purpose of a data engineer is to
increase the company’s revenue or decrease its costs.
Everything else is a distraction.

Instead of blindly executing on stakeholder requests, the best data engineers:
✅ Understand business priorities—What metrics drive the company’s success?
✅ Challenge assumptions—Is the requested data pipeline actually needed?
✅ Prioritize ruthlessly—Which initiatives have the highest ROI?

For example, let’s say a marketing team asks for a new dashboard tracking click-through rates on ads. A typical data engineer builds the dashboard without question. A high-impact data engineer asks:

  • Why do you need this dashboard?
  • What decision will it help you make?
  • Is there a more automated way to surface this insight?

Instead of just delivering raw data, they might:

  • Build an alerting system that notifies marketers when CTRs drop.
  • Enrich the data with machine learning predictions.
  • Help refine ad targeting strategies using advanced analytics.

This proactive mindset is what sets top engineers apart.

Requiring a consulting mindset

To truly stand out, data engineers need to think like consultants rather than just developers. This means:

1. Asking the right questions

Instead of just accepting tasks, dig deeper:

  • What is the business trying to achieve?
  • How will this data be used?
  • What is the simplest solution to deliver value?

2. Communicating effectively

Technical skills are table stakes—what really matters is being able to:

  • Translate technical concepts into business impact.
  • Proactively engage with stakeholders to refine requirements.
  • Tell a compelling data story, not just deliver raw numbers.

3. Taking ownership of outcomes

The best data engineers don’t just write code—they take responsibility for delivering results.
Instead of just building pipelines, they measure their success:

  • Did this data pipeline improve decision-making?
  • Did it help the company reduce costs or increase revenue?
  • Are stakeholders actually using what was built?

The shift from “I built what you asked for” → “I helped drive a business outcome” is what defines an elite data engineer.

Conclusion

Standing out as a data engineer isn’t about mastering every tool—it’s about understanding the business, asking the right questions, and driving real impact.

To differentiate yourself:
✅ Stop thinking of yourself as a pipeline builder—you are a business enabler.
✅ Challenge requests—don’t assume stakeholders always know what they need.
✅ Measure success by business impact, not by technical achievements.

A great data engineer isn’t just technical—they are strategic thinkers who understand that the best way to add value is not just through better data systems, but through better business decisions.

If you focus on business outcomes over tools, you will stand out in your career, gain influence, and unlock opportunities far beyond the typical data engineering role.