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Key Takeaways
- Data engineering is required for building data pipelines that collect, clean, and move data from various sources and is essential for real-time processing and analytics.
- Data warehousing focuses on designing and maintaining structured storage systems, making it easier for teams to run reports, access historical data, and support business intelligence.
- Hiring the right role depends on your current systems, data complexity, and goals. In many cases, businesses benefit from having both roles work together.
Hiring for data roles can sometimes feel like ordering from Starbucks. Sure, everything sounds impressive, but what’s the actual difference between a flat white and a cappuccino?
If you’re stuck figuring out whether you need data warehousing vs. data engineering, you’re definitely not alone. Understanding the distinction is important because choosing the right specialist directly impacts your business’s operational efficiency, data-driven decision-making, and even growth potential.
In this article, we’ll clarify exactly what separates these two roles, share practical tips for deciding who you need, and simplify your hiring process. By the end, you’ll confidently know whether your company should hire a Data Engineer or a Data Warehouse Architect.
Data Warehousing Explained: What Exactly Does a Data Warehouse Architect Do?
When you first hear the term “data warehouse,” you might picture a massive physical building stacked floor-to-ceiling with hard drives and servers, like something out of a sci-fi movie.
In reality, data doesn’t take up much physical space at all. In fact, the entire internet’s data weighs about as much as a tennis ball. Surprising, right?
So what exactly is data warehousing? Put simply, it’s the organized collection, storage, and management of large volumes of data from multiple sources. Businesses use data warehouses to make reporting, data analysis, and decision-making easier.
A data warehouse architect (sometimes called a data warehouse engineer) is responsible for designing and managing these digital spaces. Think of them as specialized librarians for your company’s data. Their core tasks involve:
- Structuring and maintaining data storage systems: They make sure your data is properly organized so your teams can easily find the information they need without sifting through an unstructured mess.
- Managing data integration from various sources: Data often comes from different systems and formats. Data warehouse architects pull all this data together seamlessly, making sure that nothing valuable gets lost.
- Optimizing databases for reporting and analytics: They’re also in charge of fine-tuning databases so reports and queries run smoothly, quickly delivering valuable insights without delay.
Considering all these responsibilities, it’s no wonder demand for data warehouse roles is booming. In fact, the Bureau of Labor Statistics projects over 6,000 job openings for data architects between now and 2033.
While a data warehouse architect is one specialized role, it’s not the only type of data-focused position your company might need. Data engineers are also just as important when it comes to building the infrastructure to manage data for your warehouse.
Understanding Data Engineering: The Role of a Data Engineer
If data warehouse architects design the blueprint and layout of the warehouse, then data engineers are the ones actually laying the bricks, wiring the systems, and keeping everything running smoothly. They’re responsible for building the infrastructure that delivers data where it needs to go in a fast, clean, and ready-to-use state.
At the core of data engineering is building and maintaining data pipelines. These pipelines collect information from multiple sources (like apps, websites, or sensors) and move it into storage systems like data warehouses or lakes.
However, movement is only a small part of the role. Data engineers also focus on data accessibility, quality, and reliability, making sure that what arrives is accurate, consistent, and usable.
Many also handle real-time data processing, which is crucial for businesses that rely on instant insights like fraud detection, recommendation engines, or any dashboard that updates on the fly.
In the bigger picture, data engineers sit at the intersection of IT and business operations. Their work allows analysts, data scientists, and even AI systems to access the right data at the right time, which helps leaders make informed decisions.
Hiring one requires more than just knowing their job title. You’ll want to look closely at the skills to look for when hiring a data engineer, especially as the role evolves. Increasingly, job postings ask for backgrounds in machine learning, AI, or economics.
These are fields that weren’t traditionally associated with data engineering but have grown in demand thanks to the potential of data-driven decision-making for driving growth. In fact, recent research shows that over 33% of data engineering roles now list these degrees as preferred qualifications.
When Should Your Business Hire a Data Engineer vs. a Data Warehouse Architect?
Choosing between a data engineer and a data warehouse architect isn’t always straightforward. It depends on what your business needs right now, what systems you already have in place, and how long you expect the project to run.
Let’s break it down so you can make a confident decision about hiring one or the other or hiring data professionals in general.
1. Evaluate your immediate business needs
If your business relies heavily on structured reporting, historical trend analysis, or business intelligence dashboards, you’re likely better off hiring a data warehouse architect.
On the other hand, if your team needs help collecting raw data from multiple sources, processing it in real-time, or setting up pipelines to feed dashboards or machine learning models, you’ll want to bring in a data engineer.
Companies dealing with especially large or complex datasets should also consider hiring a “big data” engineer, especially if latency, scale, or real-time processing is part of the challenge.
2. Consider your existing infrastructure
Do you already have a functioning data warehouse that just needs maintenance and optimization? A data warehouse architect can take the lead there. But if you’re still building out your system or even starting entirely from scratch, a data engineer will lay the necessary groundwork first.
If you’re somewhere in between and wondering whether to expand your in-house team or test the waters with external help, it’s worth reviewing the pros and cons of hiring freelance data engineers. In some cases, short-term freelance support is all you need to get moving.
3. Consider your budget and scale
Many businesses know they need dedicated data professionals, whether that’s an engineer or architect, but delay hiring because they feel they can’t justify US-level salaries at their current stage.
This “chicken and egg” problem means waiting to hit certain revenue targets before investing in the data talent that could actually help you reach those targets faster.
But you don’t have to wait. Nearshore hiring for data roles in Latin America can give you access to exceptional data engineers and warehouse architects at 30–70% lower salary costs compared to US rates. This allows you to bring in specialized data expertise much earlier in your growth journey.
When considering timing, don’t just ask, “Can we afford a data professional now?” but rather, “Can we afford to delay bringing in the data expertise we need?” Nearshoring often makes the answer to both questions easier.
4. Determine if it’s a short-term or long-term need
Finally, think about how long you’ll need the role. Long-term in-house hires (whether in-office or remote) make sense for ongoing data infrastructure or analytics support.
But if your needs are more project-based, outsourcing your data engineering might offer more flexibility—especially if speed and specialized skills are your top priority.
Final Thoughts
So, as you’ve seen, a data warehouse architect and a data engineer don’t just have different titles—they play very different roles in how your business collects, stores, and uses data.
In many cases, one supports the other. Without reliable pipelines, a warehouse isn’t much use. And without a well-structured warehouse, your data can end up scattered and hard to access.
Making the right hire affects more than just your tech stack. It influences how quickly your team can access actionable insights, how clean your reporting is, and how well your systems scale as your data grows.
If you’re figuring out where to start, Near helps companies find top-tier remote data talent across Latin America. Our nearshoring model makes it easier to build reliable, cost-effective teams who work during your working hours.
To learn more about the benefits of hiring from Latin America, read our article “Why You Should Hire Nearshore Data Engineers and How to Do It.”
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