Before you write a single job req, it’s worth understanding what’s actually on the line when it comes to business data. McKinsey’s research on customer analytics found that companies that use their data intensively:
- Post above-average revenue growth almost three times as often as companies that only check their data sporadically.
- Are over 2.6 times more likely to see significantly higher ROI.
- Are up to 23 times more likely to outperform competitors in winning new customers.
- Are nearly 19 times more likely to be highly profitable.
- Are 21 times more likely to move customers into their most profitable segments.
That gap doesn’t come from hiring “a data person.” It comes from hiring the right one.
A data scientist with no clean data to work with will spend their first quarter building pipelines instead of models. A data engineer dropped into a company that already has solid infrastructure will be underused. Neither outcome moves your business forward.
This guide breaks down the differences between a data engineer vs. data scientist, gives you a decision framework for hiring stage by stage, and shows you what the talent market looks like.
What Does a Data Engineer Actually Do?
A data engineer builds and maintains the infrastructure that gets data from source systems into a usable, reliable state. Think of them as the people who build the roads and pipes, without them, nothing downstream can move.
Data Engineers’ core responsibilities:
- Designing and maintaining ETL/ELT pipelines that move data from applications, APIs, and third-party tools into a central warehouse,
- Building and optimizing data warehouse and data lake architecture,
- Monitoring pipeline health and fixing failures (often before anyone else notices something broke),
- Enforcing data quality, schema consistency, and access controls as the company scales, and
- Making sure data is fast, structured, and accessible enough for analysts and data scientists to actually use it.
Data engineers’ key skills and tools
SQL and Python are table stakes. Beyond that, look for:
- Experience with Spark, Airflow, or similar orchestration tools,
- dbt for transformation,
- Hands-on work with a cloud platform like AWS, GCP, or Azure, and
- An understanding of distributed systems and the ability to reason about scale, not just write queries.
What they deliver to the business: a data warehouse hiring managers can trust one where a dashboard, a model, or a finance report all pull from the same clean source instead of five conflicting spreadsheets. If your team is hiring data engineers for the first time, the strongest LATAM profiles typically combine backend engineering fundamentals with cloud-native pipeline experience, which tends to translate cleanly to US tech stacks.
What Does a Data Scientist Actually Do?
A data scientist takes data that’s already reasonably structured and turns it into predictions, patterns, and recommendations the business can act on. Where the data engineer builds the road, the data scientist decides where to drive.
Data scientist’s core responsibilities:
- Exploratory analysis to find patterns, trends, or anomalies in existing data.
- Building and validating predictive models: churn prediction, demand forecasting, fraud detection, personalization.
- Designing experiments and interpreting statistical results.
- Translating technical findings into recommendations that product, marketing, or leadership can actually use.
Data scientists’ key skills and tools:
Python and R remain the primary languages. Beyond that, look for:
- ML frameworks like scikit-learn, PyTorch, or TensorFlow,
- A solid statistics foundation and
- Visualization tools like Tableau or Matplotlib, a data scientist who can’t communicate a finding clearly is a data scientist whose model never makes it into production.
What they deliver to the business: not raw numbers, but decisions. A well-scoped data science hire should be able to tell you, in plain language, why churn spiked last quarter and what to do about it, not just hand over a notebook full of code.
Data Engineer vs. Data Scientist: Side-by-Side Comparison
| Dimension | Data Engineer | Data Scientist |
| Primary focus | Build and maintain data pipelines & infrastructure. | Analyze data, build models, generate insights. |
| Core skills | SQL, Python, Spark, Airflow, dbt, AWS/GCP/Azure. | Python, R, scikit-learn, PyTorch, TensorFlow, statistics. |
| Key deliverable | Reliable, scalable pipelines; warehouse architecture. | Predictive models, dashboards, and business recommendations. |
| Works closely with | DevOps, Data Analysts, Data Scientists. | Product, Business, Data Engineers. |
| Hire first when… | Data is messy, scattered, or inaccessible. | Clean data exists but isn’t generating value. |
This is also where US hiring math gets uncomfortable. A tightening domestic talent pool has pushed both salary and time-to-hire up for specialized data roles, one reason more hiring managers are looking outside the U.S. entirely.
The Bigger Picture: Why This Talent Gap Isn’t Going Away
The pressure driving hiring managers toward LATAM for data roles isn’t isolated to data teams, it’s part of a broader, structural shift across US tech hiring. The real shortage isn’t developers in general; instead, junior and mid-level supply is higher than ever. The scarcity is concentrated at the senior level: engineers who can own production systems, make sound architectural calls, and, increasingly, operate AI in production rather than just experiment with it.
A few forces are compounding that gap. AI tools are amplifying what strong senior engineers can do, not replacing the need for them, which raises the bar for what “senior” even means. At the same time, tightening H-1B policy has narrowed the pipeline for bringing international senior talent onshore, right as demand for cloud, security, and AI/ML skills keeps climbing. The fully loaded cost of a senior engineer in a major US hub now regularly clears $250,000–$350,000 once salary, benefits, and overhead are factored in a number that keeps climbing for exactly the specialized roles data teams need.
That’s the environment nearshore hiring is answering. Senior LATAM engineers typically:
- Cost 30–50% less than US equivalents
- Work within 1–4 hours of US time zones.
- Can go from onboarding to a meaningful contribution within the first sprint or two, without the months-long domestic search or the salary premium that comes with it.
Which Role Should You Hire First?
No data infrastructure yet? Hire the engineer first. If your data lives in ten different tools, nobody trusts the numbers in your dashboards, or your “data team” is one analyst manually pulling CSVs, a data scientist can’t do much for you yet. There’s nothing clean to analyze. Hire the engineer, get a warehouse in place, and you’ll unblock everything downstream, including future data science work.
Have pipelines but no insights? Add the scientist. If your data is already structured and accessible but nobody’s using it to make decisions, no churn model, no forecasting, no experimentation, that’s a data scientist problem, not an infrastructure one.
Pre-PMF startups almost always need the engineer first, if they need either role at all. At this stage, most “data science” needs can be handled by a technical founder or a data-literate analyst with a spreadsheet. Don’t over-hire.
Post-PMF, scaling companies are where the “which role” question gets real. If you have paying customers, a growing product surface, and increasing data volume, you likely need the engineer to keep the lights on and, shortly after, a scientist to start extracting value from what’s been collected.
When you need both: most Series B+ companies with 100+ employees eventually need a small data team, not a single hire. The sequencing matters more than the headcount: hire the engineer 3–6 months before the scientist so there’s something for the scientist to work with on day one.
Hiring Data Talent from LATAM
If the roles are clear but the US hiring math still doesn’t work: long time-to-hire, high salaries, thin candidate pools for specialized data roles. LATAM is worth a serious look.
Why LATAM for data roles specifically: the region has built real depth in both data engineering and data science over the past several years, driven by strong computer science and engineering programs and a wave of professionals trained on modern cloud and ML stacks. Country-by-country, the strongest hubs for data talent are Brazil, Argentina, Colombia, and Mexico, each with different specialties and cost bands worth comparing before you commit to one market.
- Seniority depth: Contrary to the assumption that nearshore talent skews junior, BEON regularly places senior and staff-level data engineers and data scientists. People who’ve built pipelines at scale or shipped production ML models, not just completed a bootcamp.
- Cost vs. US benchmarks: US companies can typically reduce data hiring costs by 30–50% by sourcing from LATAM without sacrificing seniority or quality (BEON.tech internal benchmarks). On a $150,000 US data engineer, that’s a real six-figure difference over a year, one that funds a second hire or extends runway.
- Time zone alignment: LATAM works the same hours as US teams, which matters enormously for data roles specifically. Pipeline incidents and model reviews need real-time collaboration, not a 12-hour lag.
- How BEON sources and vets: BEON works from a pool of 52,000+ vetted profiles across the region, with most clients seeing their first matched candidates within 24–48 hours and a start date inside two weeks. Every profile is technically vetted before it reaches you, the screening work is already done.
Expand Your Team with the Greatest Talent
Three things to take into your next hiring conversation:
- Data engineers build the infrastructure; data scientists extract the value. You almost never need both on day one, and hiring the wrong one first stalls the other’s usefulness.
- Let your data maturity decide the sequence. Messy or missing infrastructure means hire the engineer first. Clean data with no insights being generated means it’s time for the scientist.
- LATAM removes the cost and speed constraint from the decision entirely. With 30–50% savings and candidates matched in 24–48 hours, you can hire the right role first instead of settling for whichever candidate happens to be available domestically.
Ready to see what strong data engineering or data science profiles from LATAM actually look like? Talk to a BEON data hiring expert and get your first matched candidates within 48 hours.
FAQs
Should I hire a data engineer or a data scientist first?
Hire a data engineer first if your data is scattered across multiple tools, unreliable, or difficult to access. Hire a data scientist first if you already have clean, structured data but are not using it to generate predictions, experiments, or business insights.
Does an early-stage startup need a data scientist?
Most pre-product-market-fit startups do not need a dedicated data scientist. At this stage, a technical founder or data-literate analyst can usually handle basic reporting and analysis. Building reliable data infrastructure should generally come first.
When should a company hire both roles?
Scaling companies often need both once their customer base, product complexity, and data volume begin growing. For Series B and larger companies, it usually makes sense to hire the data engineer three to six months before the data scientist so the necessary infrastructure is ready.
Why hire data engineers and data scientists from LATAM?
LATAM offers experienced data professionals with strong cloud, engineering, and machine-learning backgrounds while providing closer time-zone alignment with U.S. teams. According to BEON.tech’s internal benchmarks, companies can typically reduce hiring costs by 30–50% and receive their first matched candidates within 24–48 hours.
