Do You Need a Data Engineer or a Data Scientist? Here’s How to Know

When businesses consider data-driven decision-making, one of the first questions is: Do we need to hire a data engineer or a data scientist? Or both? 

The answer isn’t always straightforward. Many companies jump into hiring a data scientist first, only to realize later that they don’t have the infrastructure to support advanced analytics. Others hire an engineer but then struggle to derive insights from the data.

The real question is: Which role solves your specific data challenges?

This guide will help you understand what each role does, how they work together, and which one makes the most sense for your business right now. You’ll also see why your business might eventually require both.

Data Engineer vs. Data Scientist: Key Differences Employers Should Know

While both data engineers and data scientists work with the same foundational data within an organization, they interact with it in different ways and at different stages of the data lifecycle.

Before diving into the details of each role, here’s a quick comparison of their key differences:

Although their tech stacks sometimes overlap, the key difference between a data scientist and a data engineer lies in their goals.

Data engineers make data usable, while data scientists analyze it to generate actionable insights and recommendations. 

Data engineers: The architects of data

Focus and responsibilities 

A data engineer focuses on designing, constructing, and maintaining data pipelines and infrastructure that allow organizations to process large volumes of information efficiently.

This includes setting up databases, managing ETL (Extract, Transform, Load) processes, integrating multiple data sources, and ensuring that data is complete, reliable, and accessible for analysis.

Technical skills 

Data engineering is a mix of software development and data management skills. Engineers are proficient in SQL, big data tools like Spark, Hadoop, and Kafka, and cloud platforms such as AWS, GCP, and Azure.

Without their work, businesses would struggle with slow, unreliable, or incomplete data, making analysis nearly impossible.

Average salary 

While data engineers command competitive salaries in the US market, it’s worth noting that looking beyond domestic talent can significantly expand your hiring options.

Below, we’ve included both US and Latin American (LatAm) salary ranges to illustrate the potential cost difference.

This comparison isn’t about finding the cheapest talent—it’s about showing that even if your budget doesn’t stretch to US market rates, you can still access highly qualified data engineers through remote hiring in regions like Latin America.

These professionals often have comparable skills and education but work in markets with lower costs of living, creating a win-win situation where they earn excellent wages for their local economy while you build your data infrastructure at a more sustainable cost.

Data scientists: The analysts and predictors

Focus and responsibilities 

Data scientists can only start their work after the engineer provides the data in the right format. Their job is to analyze, model, and interpret that data to extract insights and patterns using statistical analysis, machine learning, and predictive modeling. 

They might use clustering techniques to group similar customers based on behavior, decision trees to predict potential business outcomes, or neural networks to detect complex patterns in large datasets.

These insights help businesses make predictions and understand how potential actions may affect them. 

Technical skills

Data science professionals use programming languages like Python and R. They also use Jupyter Notebooks for data visualization and machine learning libraries such as TensorFlow and Scikit-Learn.

Unlike data engineers, data scientists focus less on infrastructure and more on drawing conclusions, forecasting, and developing AI-driven solutions that turn raw data into strategic business decisions.

Average salary

According to the US Bureau of Labor Statistics, data scientist is one of the fastest-growing occupations, with an expected growth rate of 36% between 2023 and 2033.

As with data engineers, hiring data scientists outside the US offers a similar opportunity for cost-effective hiring without compromising on quality.

The salary comparison below shows the same pattern—significant savings potential when hiring remotely from LatAm. 

This approach has allowed many US companies to build robust data science capabilities even with limited budgets, particularly as remote work has become normalized across the industry.

‍Does Your Business Need a Data Engineer or a Data Scientist?

Making the wrong hire at the wrong time can lead to inefficiencies. If your infrastructure isn’t ready, a data scientist won’t be able to do their job effectively.

Likewise, if you already have a strong data pipeline but no one is analyzing it, hiring another engineer won’t solve your problem. Here are some guiding factors to help you decide:

Hire a data engineer if:

• Your data is scattered, messy, or inconsistent
• Your reports take too long to generate due to slow queries
• Your company lacks a central data warehouse
• You need to integrate multiple data sources (e.g., CRM, ERP, third-party data)
• Your data needs to be structured before insights can be generated
• You primarily use third-party analytics tools and don’t need to build in-house models

Hire a data scientist if you:

• Have clean, structured data but no one to analyze it
• Need predictive analytics to make better decisions
• Want to build AI or machine learning models
• Struggle with turning data into actionable business insights

The decision to hire a data engineer or a data scientist depends on the specific challenges you are trying to solve—not just general best practices. Identifying your biggest data challenge—whether it’s infrastructure or analysis—will help determine which hire will have the greatest impact. 

When (and Why) You Might Need Both Roles

As a company grows and its data operations become more complex, hiring for both roles often becomes necessary.

Below are some real-world examples of when hiring both a data engineer and a data scientist becomes critical:

Example #1: E-commerce personalization and customer retention

A growing e-commerce company wants to increase customer retention by offering personalized recommendations and promotions.

• Why a data engineer is needed: First, a data engineer must build a robust pipeline to collect and process customer transaction data, website interactions, and behavioral metrics from multiple sources. This ensures the data is accurate, structured, and accessible.
• Why a data scientist is needed: Once the data is well-organized, a data scientist can analyze customer behavior, build predictive models, and create recommendation algorithms to improve customer engagement and reduce churn.

Without a data engineer, the data scientist wouldn’t have reliable data to work with. Without a data scientist, the company wouldn’t be able to extract meaningful insights or optimize its retention strategy.

Example #2: SaaS company customer churn prediction

A SaaS startup wants to reduce churn by identifying customers at risk of canceling their subscriptions.

• Why a data engineer is needed: The engineer sets up data pipelines that pull information from multiple sources—customer usage data, support tickets, and billing records—into a centralized database.
• Why a data scientist is needed: With this structured dataset, a data scientist can build machine learning models to detect patterns in customer behavior, allowing the company to predict which users are likely to churn and take proactive steps to retain them.

If the company only needed historical reports on churn rates, a data engineer would be sufficient. But for predictive churn analysis and proactive retention strategies, a data scientist is essential.

Example #3: Streaming platform content recommendations

A media streaming platform wants to enhance user experience with personalized content recommendations.

• Why a data engineer is needed: A data engineer must build the infrastructure to process massive amounts of user activity data, including viewing history, watch times, and user preferences.
• Why a data scientist is needed: Using this data, a data scientist can develop machine learning algorithms to suggest content based on a user’s past behavior and preferences.

If the company only needed basic viewership reports, a data engineer would be sufficient. But for advanced recommendation systems, a data scientist is also required.

Final Thoughts

So, data engineer vs. data scientist—which one should you hire first?

Both roles are essential. However, depending on your business needs, one may be more critical to hire first. If your data infrastructure is weak, start with a data engineer to build reliable systems. If your data is well-organized but underutilized, hire a data scientist to extract insights.

Most companies eventually require both, but hiring the right one at the right time will save you frustration (and money).

If you’re not ready for a full-time hire and just want to test the waters, outsourcing or working with consultants or freelancers can be a smart alternative. This provides access to skilled talent and expertise without the long-term commitment of a full-time hire.

For mid-market companies and fast-growing startups who know they need long-term data expertise, “nearshore hiring” can be particularly cost-effective. Nearshore locations like Mexico, Colombia, and Argentina offer a strong talent pool of skilled data professionals with salary expectations significantly lower than US averages.

To find out more, read our article “Why You Should Hire Nearshore Data Engineers and How to Do It.” The insights it contains are relevant to hiring data engineers and data scientists.