These days, every company seems eager to fill a "data scientist" role, promising exciting opportunities to work with machine learning algorithms, predictive models, and deep learning frameworks. However, for many professionals who step into these positions, reality doesn’t quite match the allure. Instead of diving headfirst into AI or modeling complex data sets, they find themselves knee-deep in data extraction, cleaning, and preparation. Welcome to the world of data engineering—a domain many didn’t realize they had signed up for.

This phenomenon stems from a fundamental misunderstanding by companies of what they actually need. They post job listings for "data scientists" when the bulk of their work involves cleaning data and ensuring infrastructure is in place to handle it—quintessentially data engineering tasks. The result is that professionals hired as data scientists end up doing the grunt work they didn’t expect: wrangling messy data, moving it between platforms, and preparing it for analysis. Disillusionment inevitably sets in for those who expected to spend their days building machine learning models, not writing SQL queries and setting up pipelines.

For aspiring data engineers, this is a hidden opportunity. While the job market is full of companies looking for data scientists, many of these organizations need a data engineer far more than they realize. The two fields require overlapping skills, particularly in the early stages—programming, database management, and some basic statistical knowledge. However, the tasks and career paths diverge quickly. Data scientists focus on deriving insights and making predictions, whereas data engineers ensure that the data ecosystem is robust and reliable. A savvy professional can start in a data science position and pivot into a data engineering career simply by stepping up to tackle the tasks others consider beneath them.

Data scientists, especially those from highly academic backgrounds, often see data cleaning and preparation as tedious. For them, this is the “boring” side of the job—the grunt work that gets in the way of more glamorous tasks like building predictive models or applying cutting-edge algorithms. Yet, without well-structured data, those algorithms are useless. Data engineers know this well and embrace the challenge of building the frameworks that data scientists rely on. From automating the extraction and transformation of data to constructing pipelines that deliver clean, well-organized datasets, these tasks are the bread and butter of data engineering.

While some data scientists struggle to extract meaning from messy datasets, data engineers are busy building scalable systems that will save time and frustration down the line. Instead of wrestling with CSV files and complaining about SQL, the aspiring data engineer uses these tools to their advantage. They streamline processes, automate data preparation tasks, and implement robust pipelines that allow for real-time or scheduled data updates. They aren’t just moving data around; they’re building the backbone of the data ecosystem. By the time data scientists finish manually preparing their datasets, the data engineer has already automated the process, eliminating repetitive work and freeing up time for more strategic tasks.

This disconnect between job titles and job functions can create friction within teams, with some data scientists lamenting the lack of "real" data science work in their roles. But for the data engineer, this is where they thrive. While their peers debate which machine learning framework is superior, data engineers are busy implementing production-grade solutions, moving beyond ad-hoc analyses to create systems that deliver value repeatedly. They are the unsung heroes of the data world, quietly ensuring that data flows seamlessly, insights are generated efficiently, and the organization runs smoothly.

Moreover, data engineers are uniquely positioned to bridge the gap between data scientists and other business units. Once the “hard part” of data preparation is complete, they can create accessible, user-friendly applications for non-technical stakeholders. These could be dashboards, visualization tools, or web-based platforms that democratize data insights across the organization. While the data scientists are still polishing their Python scripts, the data engineer has already built something scalable, sustainable, and usable.

Ultimately, this dynamic reveals a deeper truth: many companies don’t need data scientists as urgently as they think. What they really need are data engineers who can ensure their data is structured, clean, and accessible. The insights, predictions, and models that data scientists produce are only as good as the underlying data infrastructure. So while some may continue to argue over who qualifies as a “real” data scientist, data engineers know that it’s not about the title—it’s about getting the job done.

If you’re an aspiring data engineer, this path could be your golden opportunity. By stepping into these misclassified data science roles, you can quietly build a career around solving the problems that others don’t want to touch. You can automate workflows, streamline processes, and ensure that the organization’s data infrastructure is solid and scalable. While your colleagues focus on tweaking their models, you’ll be building systems that bring real value to the company, and you’ll likely go unnoticed—until it becomes clear just how much the organization relies on the work you’ve done.

In the end, data engineers are the ones who make data science possible. And for those willing to embrace the challenge, the rewards can be substantial—not only in terms of career growth but in the knowledge that you’re the one quietly keeping the data-driven machine running.

About Me: 25+ year IT veteran combining data, AI, risk management, strategy, and education. 4x global hackathon winner and social impact from data advocate. Currently working to jumpstart the AI workforce in the Philippines. Learn more about me here: https://docligot.com