Data Engineer vs. Data Scientist: What’s the Difference?

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Rafael Timbó
Rafael Timbó
Chief Technology Officer

Table of Contents

Learn more about the differences between data engineers vs. data scientists. Along the way, we'll touch on their careers, salaries, skills, and roles.
Published on
August 8, 2022
Updated on
April 11, 2024

While both data engineers and scientists both use machine learning (ML), statistics, and business intelligence (BI) skills to help you make better business and hiring decisions, the two aren’t interchangeable roles.

Data engineers create and maintain structures and systems for gathering, extracting, and organizing data, while data scientists analyze that data to glean insights and answer questions. The two roles also have different responsibilities, salaries, and roles.

Read on to learn more about the differences between data engineers vs. data scientists. Additionally, this article explores careers in data, salaries, requisite skills, and responsibilities. Based on these findings, you can decide whether a data scientist or a data engineer is a better fit for your company.

What Is the Difference Between a Data Scientist vs. a Data Engineer?

Data science is an interdisciplinary field that uses scientific processes, methods, systems, and algorithms to extract insights and knowledge from structured and unstructured data.

As data science professionals, data scientists concentrate on spotting new insights from the data that was extracted and organized for them by data engineers. They also:

  • Conduct experiments
  • Create hypotheses
  • Use their knowledge of data analytics, statistics, machine learning (ML), business intelligence (BI), and data visualization to identify patterns and predict trends

On the other hand, data engineering involves designing and creating systems for gathering, storing, and analyzing data.

Accordingly, data engineers are responsible for:

  • Designing, building, testing, integrating, managing, and optimizing data from various sources
  • Building and testing the architectures and infrastructures that enable data generation
  • Creating and optimizing data pipelines — sets of actions that move raw data from disparate sources into a data warehouse for storage and analysis

In short, data engineers build data systems and architecture that scientists use to gather, analyze, and organize data.

Types of Data Scientists

The terms "data scientist" and "data engineer" encompass a multitude of responsibilities. Depending on industry and specialization, the role of a data scientist fits into several applications.

Machine learning scientists

As their title suggests, machine learning scientists work with ML models. Besides using ML models to extract, clean, and analyze data, they also create ML models using a mix of algorithms and data.

Actuarial scientists

Also known as actuaries, actuarial scientists use mathematics and statistics to predict financial risks for organizations. Most actuaries work in industries that rely heavily on risk management, such as financial speculation and insurance.


These data scientists apply statistical models and methods to real-life problems. Specifically, they collect, analyze, and interpret insights from data to help team leads and C-suite executives make informed decisions. Statisticians play vital roles in various industries, including healthcare, business, physical sciences, and government.

Digital analytics consultants

Digital analytics consultants gather and analyze social media and website data to help brands stand out from competitors. They're also responsible for:

  • Teaching teams how to use analytics platforms
  • Improving website performance
  • Optimizing marketing campaigns
  • Improving social media presence
  • Developing email marketing strategies

Types of Data Engineers

As with data scientists, there are multiple types of data engineers, including:


Generalist data engineers typically work in small teams with other data science professionals, like digital analytics consultants and machine learning scientists.

If generalists are one of the few or the only data science professionals at their company, they will have to take on basic data science tasks, such as collecting, processing, and analyzing data. On the flip side, if they work at a company with many data scientists, generalist data engineers will only be responsible for building and maintaining data analysis systems.

Pipeline-centric data engineers

Often found in mid-sized companies, pipeline-centric data engineers are responsible for building, testing, maintaining, and optimizing data pipelines. Pipeline-centric data engineers also collaborate with data scientists to interpret and use collected data. They usually work in bigger teams than generalists.

Database-centric data engineers

Database-centric data engineers create, maintain, and populate analytics databases. Additional responsibilities include implementing data pipelines; creating table schemas using extract, transform, load (ETL) methods; and adjusting databases for effective analysis. They often work for large organizations and conglomerates.

Languages & Tools

Because of the similarity between the two positions and the use of machine learning and business intelligence in both, there is some overlap between popular programming languages and tools. For example, Python and SQL are used across occupations as foundational languages for both data extraction and database management. 

However, in more specialized roles, data engineers and scientists benefit from using specific languages, tools, and programs that execute key functions more efficiently. Often, both occupations use a combination of these languages and tools, highlighting the need for multi-language fluency in either role.

What Languages Do Data Scientists Use? 

Data scientists require versatility in most of the languages they employ. Depending on project requirements, technical debt considerations, and specific tasks that need to be performed, data scientists commonly use some of the following languages:

  • Python: Due to its well-established ecosystem, Python provides a strong foundation for data analysis, visualization, and machine learning.
  • R: Created specifically for statistical computing and analysis, R is regularly used by most statisticians for data modeling.
  • Julia: As a newer programming language, Julia isn't as well-established as legacy languages such as Python. However, Julia caters to projects that require large-scale data manipulation.

What Languages Do Data Engineers Use?

When data engineers use programming languages, they require robust data maintenance and infrastructure capability. Data engineers commonly target some of the following languages depending on the specific nature of their project:

  • Java: Because of its strength in creating scalable data processing applications, engineers employ Java to help create structure in big data technologies.
  • Scala: This language is commonly used in tandem with the Spark framework, helping engineers to execute big data processes due to Scala’s accessible syntax.
  • Go: As a high-performing language, Go enables engineers to create low-latency microservices and applications.

What Tools Do Data Scientists Use?

Data scientists need tools and frameworks that allow for robust data manipulation and visualization. The following tools, libraries, and established frameworks provide functionality, accessibility, and a higher volume of output for data scientists: 

  • Matplotlib: For data scientists, the Python library Matplotlib provides a popular platform for creating scalable data visualizations.
  • Scikit-learn: Python’s machine learning library provides several tools for manipulation, including regression and clustering.
  • JupyterLab: Thai open-source platform allows developers to share live code for further data exploration and collaboration.

What Tools Do Data Engineers Use?

Data engineers need practical tools that assist in building and maintaining data systems. The following tools and libraries provide robust functionality and automated processes that ease the engineering workflow:

  • Apache NiFi: This open-source tool for data integration allows engineers to automate data flows between several concurrent systems.
  • Great Expectations: To ensure data quality, this open-source library validates data for errors and bottlenecks in large data sets.
  • Relational and NoSQL databases: Depending on project requirements, engineers use these databases to store structured, semi-structured, or unstructured data.

Salaries and Hiring Projections

Data professionals remain in high demand across several industries, including fintech services, media, healthcare, and telecommunications. Due in part to digital innovation and the rise of Web 3.0, both occupations are projected to grow and increase hiring volume over the next decade. The following provides a snapshot of the current hiring projections and average salaries for data engineers and data scientists: 

Data scientists

Data engineers

*These metrics are collected from separate data sources. Growth metrics for data scientists span from 2022-2032. Growth metrics for data engineers span from 2018-2028.

Note that these figures are estimates and reflect a base salary. Location, industry, and individual candidate experience may cause these estimates to change. Additionally, base pay is often increased through additional compensation such as yearly bonuses, stock options, or profit-sharing programs.

Career Projections

The typical career projections of both data scientists and data engineers are similar in titles and organizational structure, but the responsibilities and knowledge required in each role are different—with some overlap in commonly used programming languages and frameworks.

Data Scientist Career Path

Entry-Level and Junior Data Scientist

  • Responsibilities: Entry-level data scientists typically work on data analysis, data cleaning, and basic machine learning functions. They may assist in building and tuning data models, generating reports, and data visualization efforts.
  • Skills: Proficiency in programming languages like Python or R, low-level statistics, machine learning algorithms, and data manipulation

Mid-Level Data Scientist

  • Responsibilities: Mid-level data scientists often work on larger and more impactful projects. They may lead data science teams, collaborate with other departments, and have a slightly more significant role in defining data strategy and goals.
  • Skills: Advanced machine learning expertise, data engineering skills, project management abilities, and a deeper understanding of business objectives

Senior Data Scientist

  • Responsibilities: Senior data scientists are experts in their field. They tackle complex business challenges, guide junior team members, and have a strategic role in shaping data initiatives within the organization.
  • Skills: Strong leadership, communication, business acumen, and expertise in emerging machine learning technology

Data Scientist Manager

  • Responsibilities: Data science managers and directors lead teams, set overall strategy, and align data initiatives with company goals. They may have budget and resource management responsibilities. There is likely less execution in this role, but more focus on leadership capability.
  • Skills: Leadership, strategic planning, Agile development, and a strong understanding of the organization's industry and objectives

Chief Data Scientist

  • Responsibilities: At the typical peak, top data scientists may reach executive positions such as Chief Data Scientist or Chief Technology Officer. They are responsible for driving data-driven decision-making at the organizational level and ensuring data initiatives contribute to the company's overall success.
  • Skills: Strong leadership, strategic thinking, executive-level communication, and a visionary approach to data and analytics

Data Engineer Career Path

Entry-Level or Junior Data Engineer

  • Responsibilities: Entry-level data engineers usually start with tasks related to data ingestion, data cleaning, and basic ETL (Extract, Transform, Load) processes. They work on maintaining data pipelines and ensuring data quality.
  • Skills: Proficiency in programming languages like Python, Java, SQL, and basic understanding of data storage and databases

Mid-Level Data Engineer

  • Responsibilities: Mid-level data engineers work on more complex data engineering projects, often involving big data technologies and real-time data processing. They may occasionally lead data engineering teams and contribute to data architecture.
  • Skills: Advanced ETL and data pipeline development skills, expertise in data modeling, knowledge of data warehousing solutions, and experience with containerization and orchestration

Senior Data Engineer

  • Responsibilities: Senior data engineers lead complex data engineering initiatives, implement data architecture, and optimize data pipelines for performance. They often collaborate with data scientists and internal stakeholders to define data strategy.
  • Skills: Strong leadership, architecture design, and performance tuning expertise, proficiency in distributed frameworks, and experience with data orchestration tools

Data Engineering Manager or Director

  • Responsibilities: Data engineering managers and directors lead data engineering teams, define strategy, and oversee data infrastructure and architecture across the organization. Engineers at this level may also manage budgets and resources.
  • Skills: Leadership, strategic planning, team management, budgeting, and a deep understanding of the organization's data needs and goals

Chief Data Engineer

  • Responsibilities: At the highest level, some data engineers may reach executive positions such as Chief Data Officer, Chief Technology Officer, or Chief Data Engineer. They are responsible for setting overall data strategy, ensuring data compliance, and aligning data initiatives with the company's strategic objectives.
  • Skills: Strong leadership, strategic tech debt management, executive-level communication, and a cutting-edge approach to data architecture and infrastructure

Which Is Right For Your Tech Project?

Because of the specialized differences and overlap in each role, it’s often difficult to determine whether an organization needs a data scientist or a data engineer. There are several considerations to make before hiring a candidate, including organizational resources, existing workflows, and ongoing data initiatives. 

Generally speaking, there’s a more obvious need for a data engineer if an organization:

  • Doesn't have enough data: If your company only has small data datasets, hire a data engineer. A data engineer can build your company's data infrastructure and take on vital data scientist tasks, such as data extraction and analysis. A typical data scientist can't create infrastructure for moving raw data into warehouses for storage and analysis, but instead analyze data and derive insights via pre-existing systems and architectures.
  • Have a limited hiring budget: As covered above, data engineers can perform many data scientist tasks. As such, data engineers are your best pick if you can't afford to hire both.

Alternatively, data scientists are likely better suited to your company if your organization has the following:

  • An established team of data engineers: Data scientists are largely meant to expand on the data engineer role. If there is already an established team of data engineers, organizations usually hire data scientists to further analyze data. This gives data engineering teams more time and energy to focus on building, deploying, testing, maintaining, and optimizing data systems and architecture.
  • A need for specialized talent: Data engineers are usually only familiar with general data science tasks. For highly specialized tasks such as social media analysis, companies should hire data scientists with the requisite skill set, and experience.
  • Adequate budget: Data scientists are often costly to hire, plus they need data engineers to properly analyze, store, and manipulate data. Most organizations need to hire at least one data engineer before hiring data scientists.

Hire Data Scientists or Engineers with Revelo

Data scientists and engineers can both use machine learning, business intelligence, and complex statistics to provide simple, visualized, and data-driven solutions. Their differences, however, provide insight into prudent hiring decisions and data initiatives.

Data scientists can't optimally function without engineers, as they’re responsible for extracting actionable insights and trends from data. While data engineers may perform data science tasks, their main goal is to build architecture and systems for storing and analyzing data.

Hire data scientists and data engineers with Revelo to establish a strong data strategy in your organization and identify cutting-edge industry trends through data analysis. After hiring, Revelo assists in ongoing administrative tasks such as benefits administration, payroll, local compliance, and taxes—allowing you to focus on core business objectives.

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