Data Engineering vs. Data Science: Where Should U.S. Companies Invest First?
If you're building or scaling a data team in 2025, you've probably already faced the classic question:
Should we hire Data Engineers or Data Scientists first?
It’s not always a simple decision. And while the roles are distinct, their success is completely interconnected. In this article, we explore when to prioritize Data Engineering, when Data Science should take the lead, and how to structure your hiring strategy for long-term impact.
Why Data Engineering often comes first
It’s tempting to go straight for the “insight” hiring Data Scientists with the goal of building predictive models, dashboards, and advanced analytics. But here’s the reality: if your data isn’t structured, accessible, and reliable, even the most skilled Data Scientist will spend more time wrangling than modeling.
In U.S. businesses, especially where datasets are often spread across cloud platforms, legacy systems, and real-time pipelines, a solid data foundation is essential.
You should prioritize Data Engineering if
Your data is inconsistent, siloed, or not readily accessible
Data pipelines are slow, fragile, or don’t scale
There’s no version control, monitoring, or governance built in
Business teams are waiting days (or weeks) for usable data
You want to enable real-time analytics or machine learning workflows
Imagine hiring a top-tier Data Scientist who then spends 80% of their time fixing broken ETL processes or validating datasets. It happens more than you’d think and it’s a costly mistake.
When Data Science Drives the Value
Once your infrastructure is in place, data is clean, accessible, and flows smoothly, Data Scientists can really do their job.
That means:
Building machine learning models that support better decision-making
Delivering insights that drive revenue or reduce risk
Automating business processes through intelligent data-driven workflows
Uncovering patterns and predictions that give your company an edge
You should prioritize Data Science if:
You already have clean, accessible, well-governed data
Your data engineering team has established scalable pipelines
You want to move from descriptive to predictive analytics
You’re ready to explore AI and machine learning use cases
You need advanced insights to inform strategic decision-making
In many cases, you’ll need both, just not all at once
Most U.S. companies are somewhere between building a data foundation and extracting real-time insights.
Depending on your maturity, here’s how to decide who to hire first:
Scenario 1: You’re early in your data journey
Hire first: Data Engineers
Why: They’ll build the infrastructure that makes future analytics possible.
Scenario 2: You’ve already got a strong data platform
Hire first: Data Scientists
Why: You’re ready to turn raw data into models, forecasts, and real insights.
Scenario 3: You need real-time, AI-powered decision-making
Hire both strategically
Why: You’ll need data engineers to maintain pipelines and data scientists to build models on top of them.
Hiring Tips for Building the Right Team
Whether you’re hiring in-house, working with contractors, or scaling through a recruitment partner, here are some best practices we see working in U.S. teams:
1. Don’t expect one person to do both.
Yes, there are some full-stack data pros out there — but long-term, specialization wins. Hire engineers to build systems, and scientists to extract value.
2. Clarify your job descriptions.
Too many job posts ask for “Data Scientists” when what they really need is an ML-savvy Data Engineer (or vice versa). Misaligned titles confuse candidates and slow hiring.
3. Look for adjacent skills, not unicorns.
An Analytics Engineer with strong SQL and dbt experience might grow into a Data Scientist. A Backend Engineer with Python and CI/CD exposure could pivot into MLOps. Trainability matters.
4. Focus on real-world data experience.
In the U.S. market, applied experience matters more than academic credentials. Find candidates who’ve worked with messy, live data — not just polished datasets in coursework.
5. Prioritize collaboration and context.
The best Data Scientists and Engineers don’t work in silos. They collaborate with product, ops, compliance, and leadership. Soft skills make a big difference in business outcomes.
The Bottom Line for U.S. Hiring Teams
Hiring Data Engineers and Data Scientists isn’t a “which one is better?” decision. It’s about sequencing, strategy, and scalability.
If you're still building your foundation start with engineers.
If you're ready to push the envelope with AI and modeling bring in scientists.
And if you want to build a truly data-driven company?
Invest in both, with a structure that allows them to succeed together.
Need Help Building Your Data Team?
At KDR Talent Solutions USA, we specialize in helping businesses across the States hire high-performing data engineers and data scientists from Databricks experts and cloud-native engineers to applied ML specialists.
We know the market
We speak your language
And we won’t waste your time
