Data Scientist vs Data Engineer: Key Differences and Career Insights

Below, we explore the key differences between data scientist and data engineer roles.

Key Takeaways

• Data engineers are responsible for building and maintaining data infrastructure and managing big data to prepare it for analysis.
• Data scientists analyze data and glean actionable insights from complex problems and future trends.
• Data science candidates need a more comprehensive technical background compared to data engineers. They also need more experience and advanced education.
• Data science salaries are generally higher than data engineers. Though both roles are currently in demand in the tech and finance sectors, the environment for data engineers is currently more stable.

Data Scientist vs Data Engineer

Data scientists and data engineers work with data to help organizations achieve their goals and make informed decisions.

While they share some overlapping skills, their job responsibilities and daily tasks differ significantly.

• Data Engineers: Collect and prepare data, developing and maintaining data infrastructures such as databases and large-scale processing systems.
• Data Scientists: Interpret data using advanced techniques like predictive modeling and machine learning to analyze, test, clean, and optimize it for solving complex problems.

What does a data scientist do?

Data scientist roles and responsibilities differ from company to company.

Data scientists are skilled in advanced statistical methods, machine learning, and predictive analytics, which they use to extract insights from large datasets.

For example, companies like Meta generate 4 petabytes of data daily, and their data science team organizes and analyzes this vast amount of information.

A data scientist could help Meta understand user behavior and determine specific actions to address user needs. Business strategy teams, product managers, and engineers then create solutions based on these insights.

Key Responsibilities of Data Scientists

Data scientists:

• Collect and manage data from multiple sources
• Perform advanced data analysis
• Develop algorithms and models to predict future outcomes
• Create visualizations to communicate findings

Data preparation involves sourcing, processing, and modeling data for analysis.

Data scientists frequently collaborate with other teams, including data engineers, to integrate predictive analytics into business operations.

This helps businesses:

• Anticipate and respond to market dynamics
• Make informed decisions
• Identify trends and patterns
• Optimize operations
• Improve customer experiences

Strong communication skills are necessary for data scientists to explain their models and predictions to non-technical stakeholders. This requires technical expertise and the ability to translate data insights into plain English.

What does a data engineer do?

A data engineer's primary role is to process data, making it ready for analysis by data scientists.

Unlike data scientists who analyze data, data engineers handle raw data, often containing human or instrument errors.

Data engineers design and build data infrastructure, creating data pipelines that help data reach the right team.

For example, building a tool that collects user data from a mobile app and making it accessible to a product team.

Typical Job Responsibilities

• Building and maintaining data infrastructure
• Managing large datasets and processing systems
• Collaborating with various teams to meet their data needs
• Managing data storage

Data engineers make sure that the infrastructure they build supports both data scientists and non-technical stakeholders.

Specialized Data Engineering Roles

• Storage Specialists: Focus on databases, data warehouses, and big data systems.
• Pipeline Specialists: Expertise in engineering and creating data pipelines.
• Analytics Specialists: Proficient in data analysis in addition to data engineering work.
• Generalists: Skilled in all the above areas.

For example, at a music streaming company like Spotify, a data scientist might need data on music genres or artists to identify upcoming listening trends.

The data engineer prepares this data. They might remove streams generated by bots. Or they might confirm that streaming data matches other internal records.

From here, a data scientist can confidently predict listening trends.

Education

Data engineers typically have a bachelor's degree in computer science, software engineering, or information technology.

While these are standard educational paths, candidates with unconventional backgrounds, such as biology or chemistry, often pivot to data engineering, learning necessary skills along the way.

A master's degree in data analytics can enhance a data engineer's qualifications, providing real-world applications and experience.

Data scientists usually require advanced degrees, such as a master's or PhD, in fields like data science, computer science, mathematics, or statistics.

These programs build deep technical knowledge needed for complex data analysis and machine learning tasks.

Essential Skills and Tools

While skills for data engineers and data scientists often overlap, data scientists require stronger business, communication, and machine learning skills to present solutions to non-technical stakeholders.

Key Skills for Data Scientists

Data scientists need to be proficient in machine learning and predictive modeling, using techniques like:

• Neural networks
• Regression
• Decision trees

Familiarity with coding languages like Python, R, SPSS, and SQL is important. These are commonly used for data analysis and visualization.

In addition to traditional programming and machine learning coding skills, data scientists should have a strong understanding of:

• Big data technologies like Apache Hadoop and Spark. These technologies handle and process large datasets efficiently
• Developing data models
• Creating algorithms
• Business sense and product metrics

Key Skills for Data Engineers

Data engineers need a solid understanding of data modeling and data storage techniques.

They must also be able to maintain an effective ETL system that can funnel data from various sources. Some common tools they may use on the job include:

• SQL
• Data warehouses like Redshift and Panoply
• Big data systems like Hadoop and Spark
• Cloud-based databases like AWS RDS and Microsoft Azure

The most common coding languages used by data engineers, primarily for creating data pipelines, include:

• Python
• Java
• C++
• Scala
• Ruby

Proficiency in AWS Cloud Services is also a must.

Depending on the role, some data engineers will work closely with data scientists and other data professionals, requiring strong communication and analytical skills, as well as familiarity with setting up and maintaining an ETL system and knowledge of BI tools like Power BI and machine learning libraries like Spark.

Roles and Responsibilities

Data scientists and engineers play vital roles in using Big Data to solve problems and enhance business operations. They often collaborate closely, leveraging their complementary skills to maximize the value of data.

Data Scientist Responsibilities

Data scientists focus on:

• Uncovering patterns in data
• Using predictive analytics to forecast future trends and identify potential operational bottlenecks
• Developing machine learning models to anticipate customer behaviors and market dynamics

The key responsibilities of data scientists include:

• Developing tools and processes that ensure data accuracy and reliability
• Creating sophisticated algorithms and models that automate complex processes, driving efficiency and innovation within the organization
• Integrating machine learning models into various business operations to optimize resources and predict critical events

Data scientists also contribute significantly to business growth by:

• Embedding data-driven decision-making into products and services
• Analyzing large datasets and deriving actionable insights
• Helping businesses innovate and develop new strategies for competitive advantage

Data Engineer Responsibilities

Data engineers focus on:

• Preparing data for analysis and modeling
• Making certain data architecture supports the company's needs
• Cleaning and organizing data to improve quality and efficiency
• Developing data processes for mining, modeling, and production

Data engineers have three primary responsibilities:

• Create and maintain data models and infrastructure
• Build data pipelines
• Prepare data for analysis

Data engineers also provide substantial business value by:

• Delivering clean, high-quality data to analysts and other stakeholders to make decisions
• Giving business leaders a deeper and more nuanced understanding of the challenges they are facing or the customers they are serving
• Developing practical, data-based solutions to problems that promote growth

Job Descriptions

Here are some sample job descriptions for data scientist and data engineer positions at Amazon.

Data Scientist Job Description

A job posting for a data scientist with Amazon's payment products team lists the following day-to-day responsibilities:

• Develop and apply new machine learning algorithms.
• Use expertise in supervised and uplift learning algorithms to improve ML performance.
• Scale optimization techniques to drive business value.
• Design A/B tests and conduct statistical analysis of their results.
• Work closely with internal stakeholders like business teams, engineering teams, and partner teams, aligning them with your focus area.
• Present and publish scientific research, contributing to Amazon's science community.
• Work with distributed machine learning and statistical algorithms to harness large volumes of data at scale to serve customers.
• Implement and operate stable, scalable data flow solutions from production systems into end-user-facing applications.

Data Engineer Job Description

A job posting for a data engineer at Amazon lists the following day-to-day responsibilities:

• Design, implement, and support data warehouse/data lake infrastructure using AWS big data stack, Python, Redshift, QuickSight, Glue/lake formation, EMR/Spark, Athena, etc.
• Develop and manage ETLs to source data from various financial, AWS networking, and operational systems and create a unified data model for analytics and reporting.
• Create and support real-time data pipelines built on AWS technologies, including EMR, Glue, Redshift/Spectrum, and Athena.
• Collaborate with other engineering teams and product/finance managers/analysts to implement advanced analytics algorithms that exploit rich datasets for financial model development, statistical analysis, prediction, etc.
• Conduct continual research on the latest big data and visualization technologies to provide new capabilities and increase efficiency.
• Use business intelligence and visualization software (e.g., QuickSight) to develop dashboards for senior leadership.
• Empower technical and non-technical internal customers to drive their own analytics and reporting (self-serve reporting) and support ad-hoc reporting when needed.
• Work closely with team members to drive real-time model implementations for monitoring and alerting risk systems.
• Manage numerous requests concurrently and strategically, prioritizing when necessary.
• Partner and collaborate across teams/roles to deliver results.
• Mentor other engineers, positively influence team culture, and help grow the team.

How to get a job

If becoming a data engineer sounds like the right fit:

• Get a bachelor's degree in computer science, software engineering, or information technology.
• Learn Python, SQL, Java, C++, and Scala.
• Develop an understanding of database management and AWS.
• Bolster your skills with additional training and certification through Microsoft, Google, or a university.

If data science sounds like more your speed:

• Choose a STEM major in computer science, mathematics, or a related field.
• Consider pursuing an advanced degree in statistics or computer science.
• Learn Python, SQL, and R.
• Familiarize yourself with machine learning, deep learning, and data visualization.
• Build a portfolio to showcase your skills. Contribute to open-source projects and tidy up your GitHub pages.
• Gain practical experience by working on real-world data science problems in internships.

Strengthening Your Application

You’re ready to apply for your dream job, but many candidates have similar qualifications.

How can you stand out?

Tailor your portfolio to the company and job description.

• Ensure your GitHub profile or blog features a few relevant projects. Include diverse projects, such as cleaning data from data.gov, performing exploratory analysis on a Kaggle dataset, or completing a machine learning project.
• Your code should be visible and well-documented. Include a README file that explains the setup and summarizes the project.
• Communication is key for both data science and data engineering roles. Practice presenting insights for a non-technical audience using a variety of storytelling and data visualization methods.

Portfolio Tips

• Emphasize the projects' impact.
• Don't be afraid to get creative. This is the perfect opportunity to showcase your data visualization skills.
• Practice telling a story through your portfolio projects. Walk the reader through your process and ensure the story has a beginning, middle, and end that communicates the impact of your work. Think of your portfolio as a test run for the regular presentations you'll make on the job. It will showcase your curiosity and passion for your work, as well as your ability to communicate clearly.

Salary Comparison

The salaries for data professionals fluctuate based on their roles, seniority, and location.

Data scientists typically earn more than data engineers due to their advanced technical skills and the complexity of their tasks.

This salary information was collected from Glassdoor and Levels.fyi.

Data Scientist Salary Range

In the United States, data scientists typically start with salaries around $90,000 and can earn up to $300,000 or more annually at senior levels.

The median annual wage for data scientists is $165,000. This reflects the high demand and skills required for this role.

Data Engineer Salary Range

Data engineers typically start with a salary of about $88,000 per year and can earn as much as $200,000 or more annually with several years of experience.

The average salary for a data engineer is about $130,000 per year.

Career Paths

How do most candidates become data professionals?

Career Path for Data Scientists

Data scientists often start their careers through self-teaching, online courses, and personal projects, gaining foundational knowledge and practical experience.

They may begin as research assistants or junior data scientists, gradually advancing to more specialized roles focusing on big data, machine learning, and AI. As they gain experience, data scientists can transition into senior roles, leading complex projects and mentoring junior team members.

They may also specialize in areas like big data engineering or machine learning.

Career Path for Data Engineers

Most data engineers don't start their careers in a data engineering role.

Many begin as software engineers, data analysts, or business intelligence analysts before moving into data engineering.

As they advance, data engineers can take on more managerial or specialized roles, such as solutions architect or data architect.

Industry Outlook

As more businesses recognize the benefits of strong data teams, the demand for data professionals remains high.

Job Outlook for Data Scientists

The integration of AI in the workplace has increased the demand for data scientists, especially those specializing in areas like deep learning.

The Bureau of Labor Statistics projects a 35% growth in employment of data scientists between 2022 and 2032, much faster than average.

The tech and finance industries, in particular, offer numerous opportunities for data scientists.

However, as the demand for data professionals continues to grow, jobs are becoming more specialized. Companies are looking to hire a team of specialists rather than one data scientist who can do it all.

This means that while there is still a high demand for data scientists, the role is evolving and merging with others.

Job Outlook for Data Engineers

Although some worry that AI will replace data engineers, it will actually make them more necessary.

Companies will need more capable professionals to develop and manage complex data systems. Rather than replace jobs, more companies will rely on data engineers to work alongside machine learning systems to effectively clean and process large datasets.

The job growth rate for engineers between 2018-2028 is projected at 21%, amounting to about 284,100 new jobs.

FAQs

What is the difference between a data scientist and a data engineer?

Data engineers are responsible for building and maintaining data infrastructure. They clean and prepare data for analysis, while data scientists analyze and present their findings to company stakeholders.

What skills do data scientists need?

A data scientist's main responsibilities include using methods like machine learning and predictive analysis to extract insights from data, addressing complex problems, and forecasting future trends. Data scientists typically need an advanced degree and experience in predictive analysis, machine learning, data visualization, programming languages like Python and R, and strong communication skills.

What skills do data engineers need?

Data engineers need a firm understanding of data modeling and data storage techniques, proficiency in tools like SQL, data warehouses (Redshift and Panoply), cloud-based databases, and big data systems (Hadoop and Spark).