With the proliferation of data science, many new roles have emerged beyond the now-popular role of data scientists. Data engineer vs. data scientist, machine learning engineer vs. data scientist, software engineer vs. data scientist, and data analyst vs. data scientist — how do all these roles differ? Of the many new roles that have emerged as a subset of data science activities, data engineering roles have been witnessing rising demand.
With rapid growth and expansion, data science has now become so advanced in its breadth and depth of applications and scaled to such a large level that multiple data science professionals are required to achieve project or business goals.
In this article, we delve into the various facets of the role of a data engineer vs. data scientist, including responsibilities, skill requirements, salaries, and career outlooks. We will uncover areas of similarities and differences between these two roles.
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We cover the following topics:
- Data Engineers vs. Data Scientists — Differences in the Roles and Responsibilities
- Importance of Data Engineers vs. Data Scientists to Businesses
- Data Engineers vs. Data Scientists — Skills Required to Become One
- Data Engineers vs. Data Scientists Salaries — How Much Do They Earn?
- Data Engineers vs. Data Scientists — Career Outlook
- FAQs on Data Engineers vs. Data Scientists
Data Engineers vs. Data Scientists — Differences in the Roles and Responsibilities
The very fact that data engineers and data scientists exist as two separate job titles indicate that these roles serve different purposes and objectives. However, the distinction between the roles and responsibilities of a data engineer vs. data scientist is greatly influenced by the organizational setup, its requirements, and how well defined the roles are. Often there exists an overlap in functions, responsibilities, and expectations.
The primary difference between data engineers vs. data scientists:
- Data scientists primarily work with big data, analyzing, processing, and modeling it to draw meaningful and useful information from unstructured inputs. This information is then interpreted to solve problems or aid decision-making or act as useful informational input in other processes, all within the context of organizational or project needs.
- Data engineers, on the other hand, create the systems, infrastructure, and architecture needed to obtain, store, generate and prepare raw data to be used by data scientists to carry out their functions and fulfill the data science requirements of an organization.
Understanding the primary responsibilities of each role can help you better gauge how data scientists differ from or compare to data engineers.
Role and Responsibilities of a Data Scientist
- Gather, collect, mine, or extract data through viable and efficient processes
- Clean, process, validate, or prepare data.
- Process and analyze data using machine learning, artificial intelligence, statistical data modeling, predictive analysis, and other similar methods and tools
- Create data models and algorithms
- Analyze, refine, and interpret results of data studies
- Draft actionable insights based on interpreted data
- Present findings through data visualization tools such as dashboards, slide decks, etc.
- Automate routine processes
- Identify problems or opportunities in large amounts of messy data.
- Solve complex problems by developing data-oriented solutions to business problems
- Identify trends or patterns in data to make it meaningful and useful.
- Create data models; develop predictive models.
- Create machine learning algorithms
- Perform open-ended research
Role and Responsibilities of a Data Engineer
- Collaborate with management and other data stakeholders to identify business or project requirements
- Create data infrastructure to store and manage data; design and develop databases, systems, processing, and analytic infrastructure, and servers
- Identify relevant data sets and data sources as per organizational requirements.
- Execute ETL processes:
A. Extract voluminous data from various sources and systems and store it in data warehouses; create and curate data marts
B. Transform data by converting it from source formats to single, viable, useful, structured formats
C. Loading and logging data into destination files
- Preprocess, clean, prepare raw, disconnected data to be used by data scientists
- Create algorithms; Deploy ML algorithms.
- Design and build data pipelines
- Design and build systems and tools for data analytics, data delivery, and data access
- Assist with and resolve technical issues related to data and data infrastructure
- Understand compliance policies
- Enhance data reliability, quality, and security
- Optimize and maintain all data processes, systems, infrastructure, and architecture created for scalability and efficiency
- Re-design data architecture to meet new business needs
Importance of Data Engineers vs. Data Scientists to Businesses
Data engineers vs. data scientists — do companies prefer one over the other? Despite focusing on separate areas, data engineers and scientists are interdependent roles. Data engineers complement data scientists, and data scientists rely on data engineers.
Both data scientists and data engineers play valuable roles in organizations, and their growing demand underlines the importance of both professionals.
Benefits of Data scientists
In a data-driven, digital-first world, data scientists bring invaluable advantages to businesses, as outlined below:
- Develop new product features based on data studies
- Discover business opportunities or areas of improvement
- Develop effective marketing campaigns
- Enable data-driven decisions to be made
- Enable recruitment automation
- Help companies get a competitive edge
- Increasing sales and company growth
- Improve business operational efficiency
- Understand and predict customer behaviors; enhance product offerings
- Perform market and competitor analysis and gather insights
Benefits of Data Engineers
Without data engineers, data scientists would not be able to function at maximum efficiency or at all in cases where data scientists are not equipped with data engineering skills. The quality of output for data scientists depends on the data they work on and the systems and infrastructure available. Skilled and efficient data engineers enhance work done by data scientists. This is a key reason why the demand for data engineers is growing.
- Necessary for data scientists to function
- Capture correct and useful data sets from the right sources
- Improve the efficiency of data science teams and processes
- Enhance the usefulness of data, making data outcomes more reliable and useful, thereby enhancing a business’ competitiveness
- Enable companies to achieve data science-dependent business outcomes faster and in a more impactful way
- Enhance IT security; minimizes cyber threats and attacks
- Enhance the Software Development Life Cycle
- Enable real-time data-based, data-driven decisions to be executed
- Help uncover business opportunities
Data Engineers vs. Data Scientists — Skills Required to Become One
Data engineers and scientists have a lot of overlapping skills and knowledge since they both function within the same data science space. However, some prominent skills differences of data engineers vs. data scientists are:
- Data scientists focus more on mathematical, statistical, and analytical skills to interpret data, while data engineers focus on coding and programming skills.
- Data scientists focus on machine learning and artificial intelligence, while data engineers focus on distributed systems and databases.
- Data scientists need to be good at identifying trends, patterns, and solutions and interpreting results, while data engineers need to be good at understanding data-related needs and building infrastructure and architecture.
- Data scientists need strong data visualization and storytelling skills.
Top Skills Needed to Become a Data Scientist
- Data Mining
- Data Wrangling
- Machine Learning
- Artificial Intelligence
- Python, R Java, C++, Scala
- SQL, Pig, Hive
- Predictive Modeling
- Coding; Programming
- Math and Statistics — Linear Algebra, Bayes Theorem, Geometry, Multivariable Calculus, Probability, Discrete Math, Graph Theory
- Tableau, Excel, Microsoft Power BI, Plotly, Qlikview, Zoho Analytics and other Business Intelligence, and Data Visualization tools
- Hadoop, Apache Spark, Apache Kafka, TensorFlow, Pandas, Matplolib, Scikit-Learn, Spark MLib, Numpy, AWS Deep Learning AMI, and other data frameworks
Top Skills Needed to Become a Data Engineer
- Strong coding and programming languages skills; Python, Scala, Java, C, C++, C#, .Net, Ruby, Perl, Golang, SAS, MatLab, R
- UNIX, Linux
- SQL, NoSQL, MySQL, Postgres, Relational Databases
- Strong ETL skills; SSIS, SSRS, PowerCenter, Data Stage
- Big Data Technologies; Apache Kafka, Spark, Hive, Hadoop, Cassandra
- Google Cloud, GCP; AWS; Redshift; S3, EC2, RDS
- ELK Stack; APIs; Oracle; Git; Snowflake; Tableau
- Storm, MLib, Spark Streaming
- Software Engineering; Agile, Scrum
- BI, Platform Engineering
- Luigi, Airflow, Azkaban
- Basic ML knowledge
Data Engineers vs. Data Scientists Salaries — How Much Do They Earn?
Salaries and total compensation for data engineers vs. data scientists are influenced by several factors, key of which are:
- Skills; Education; Certifications
- Job Levels
- Job / Company Location
- Employing Company and Industry
- Market demand and supply of talent
Data scientists appear to earn more than data engineers since there has been an increasing number of data scientists jobs over the last few years. However, information from various sources on these roles reveals that data engineering jobs are now enjoying great demand as data teams become more focused on specialized skills.
The data below has been parsed from different sources (mentioned below) to gauge earning levels for data scientists and data engineers. These figures, however, depend on how much value a company places on each of these roles in the context of their own operations, business needs, and the industry in which they operate. They also depend on the various other factors that influence salaries.
However, a quick look at salaries for data engineers vs. data scientists indicates that they are comparable, and where one role appears to pay more than the other, the salary gap is not marked. Salaries for both roles are attractive and make for lucrative options when considering either as a career path.
Average Data Engineer vs. Data Scientist Salary in the US 2022
We compiled data from Glassdoor and Built In, two popular sites that report on salary data in the tech industry, to compare the average annual salaries of data engineers vs. data scientists in the present years in the United States. (Note: Data is only representative with a differing number of salaries reported for data engineers vs. data scientists)
Average Data Engineer Salary in the US 2022
- According to Built In, the average data engineer salary in the US 2022 is $121,048 per year
- Glassdoor lists the average data engineer salary in the US 2022 as $112,493 per year
Average Data Scientist Salary in the US 2022
- According to Built In, the average data scientist salary in the US 2022 is $123,419 per year
- Glassdoor lists the average data scientist salary in the US 2022 as $117,212 per year
Facebook Data Engineer vs. Data Scientist Average Salaries by Job Levels
Data science jobs are highly sought after at Facebook. A look at Facebook’s pay scale for data engineers vs. data scientists at different levels gives us an idea of how salaries and total compensations compare between the two roles.
Data Engineer vs. Data Scientist — Average Salaries at Apple, Amazon, Netflix, Google
Below is a comparison of the average base salaries paid to data engineers vs. data scientists at other FAANG companies based on information parsed from Glassdoor.
Data Engineer vs. Data Scientist — Career Outlook
The number of jobs in data science is projected to grow in the upcoming years as businesses become more data-centric. The US Bureau of Labor Statistics projects a 27.9% growth in data science-related employment through 2026. With the rise of new technologies such as blockchain, crypto, metaverse, IoT, and Web 3, big data is expected to grow exponentially and present many business opportunities.
As this trend grows, both data scientists and data engineers will continue to be in demand. When coupled with a shortage of skilled talent, data science professionals can command premium salaries.
Demand for Data Engineers vs. Data Scientists
Earlier, companies focused on hiring data scientists to meet their data science needs. However, with companies having to manage data on a large scale, data teams are becoming the norm. This, in turn, is creating more job listings for data engineers vs. data scientists. Increased adoption of cloud technologies will also enhance requirements for data engineers vs. data scientists to handle data infrastructures.
Getting in early and riding the wave can have a positive compounding effect on career progressions in both roles. In the coming years, new talent attracted by the lucrative opportunities in data science will increase supply and close existing skill gaps. However, by embarking on a data engineer or data scientist career path now, you can accumulate years of valuable experience. This will enable you to move into highly rewarding, high-level positions which will be in demand in the future as the data science market expands and matures.
FAQs on Data Scientists vs. Data Engineers
Q1. Can I move from a data engineer role to a data scientist role?
Yes, by broadening your computer science skill set to acquire the required skills to function as a data scientist vs. a data engineer, you can switch career paths. Enhance your mathematical, statistical, analytical, and machine learning skills and cultivate a strong business mindset.
Q2. Who makes more money - a data engineer or a data scientist?
Earnings between data engineers and data scientists are comparable. However, with growing demand and a short supply of talent, skilled data engineers are able to command premium salaries. Salaries for data scientists and data engineers depend on the hiring organization and industry; some companies place more value on data engineers vs. data scientists.
Q3. How are interviews conducted for data engineers vs. data scientists?
Companies usually follow standardized and similar interview processes for data engineers and data scientists. However, questions asked will assess skills unique to each role. The system design interviews are emphasized more for data engineers.
Q4. How do the roles of machine learning engineers vs. data scientists differ?
Data scientists analyze data to generate meaningful information for data-driven decision-making. Machine learning engineers create code, algorithms, and software to allow computers to perform tasks independently. They put data scientists' models into production.
Q5. How do the roles of software engineers vs. data scientists differ?
Data scientists analyze data for insights while software engineers develop software programs and applications. Data scientists are usually required to possess specific minimum educational qualifications and experience for the role, while software engineers can be self-taught.
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