Data has become a pivotal part of business decisions around the world today and as such, enterprises are investing heavily in deductions gotten from the data they generate to come up with analysis and patterns to help them in predicting future outcomes to better the profitability of the company.
As a result of this gold rush in data analysis, there are several emerging roles and responsibilities being created to cater to specific needs found in the data industry and it is not only believed that useful insights can be gotten from analyzing data but also that the quality of these insights is dependent on the processes employed by data professionals. One such type of comparison is the Data Engineer vs Data Scientist one.
This article aims at relaying and exposing you to the important roles Data Engineers and Data Scientists play in the Big Data industry and show you the key differences between them so you can make the Data Engineer vs Data Scientist decision in an informed manner. Read along to find out the main characteristics of both job roles so that you can decide when the profession suits you.
Table of Contents
What is a Data Engineer?
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A Data Engineer builds and optimizes a system for analytical purposes. The engineer does this by gathering relevant data, developing, constructing, and testing them by building data pipelines so that incoming data can be prepared for operational or analytical purposes by Data Analysts and Data Scientists. The Data Engineer also maintains architectures such as databases and large-scale processing systems.
Data Engineers are usually regarded as the intermediaries between the Data Analysts and the Data Scientist as they are the link between the different, semi-structured, unstructured raw data that may contain human, machine, or instrument errors and creating structured formats, scaling the data, extracting, transforming it into useful formats, and loading (ETL) the data for easy usage and interpretation.
Data Engineers usually hail from Software Engineering backgrounds and are proficient in programming languages as this will be needed to build and improve data reliability, efficiency, quality, and recommending methods to achieve this.
To learn more about Data Engineers, click this link.
What is a Data Scientist?
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A Data Scientist is one whose specialty is analyzing, testing, aggregating, and optimizing data to come up with useful insights that would be mostly presented to a company’s or organization’s board, stakeholders, marketing team, individuals, etc from the data that was cleaned and prepared for them by the data engineers.
Data Scientists do this through their expertise in statistical methods and building machine learning models to make predictive and prescriptive analytics as well as answer important business questions.
They employ advanced data techniques such as neural networks, clustering, Decision trees to discover hidden patterns, develop actionable patterns, and train predictive models from cleaned data.
They usually come from mathematics and statistics backgrounds and would need to know about Machine Learning and distributed computing to gain access to the data and come up with visually appealing reports.
To learn more about Data Scientists, click this link.
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Factors that Drive the Data Engineer vs Data Scientist Decision
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Now that you have a basic idea of both roles, let us attempt to answer the Data Engineer vs Data Scientist question. There is no one-size-fits-all answer here and the decision has to be taken based on the parameters listed below. Both these roles carry their own importance and it is difficult to say which one is better. The following are the key factors that drive the Data Engineer vs Data Scientist:
1) Data Engineer vs Data Scientist: Roles
Data Engineer
Data Engineers are saddled with the responsibility of establishing the foundation on which Data Analysts and scientists build. They are tasked with designing, building, integrating, testing, managing, and optimizing data from various sources.
They are architects that build infrastructures that enable data generation, they focus on creating free-flowing data pipelines by combining diverse data technology and programming languages to achieve real-time analysis.
Data Scientist
Data Scientists are usually the ones that provide insights into data by conducting high-level research and analysis on the data to identify trends and opportunities that will help the growth of the enterprise.
They interact with the data structures built by the Data Engineer to derive outcomes by asking questions and querying the systems using advanced statistics and algorithms to produce reliable predictions about possible future outcomes.
2) Data Engineer vs Data Scientist: Responsibilities
Data Engineer
The Data Engineer’s focus is to build and create systems where data can be infused and is tasked with the following responsibilities: Building APIs for data connection, creating avenues for the integration of external and new datasets into existing data pipelines by building complex queries, monitoring and continual testing of the systems to make sure it works optimally, providing features for Machine Learning models, etc.
Data Scientist
The main focus of a Data Scientist is to come up with a creditable analysis of data generated by an organization to provide useful insight hence, they have the following responsibilities: using statistical models to evaluate and validate the data for analysis, integrating data, and performing ad-hoc analysis, building predictive models by using machine learning algorithms, creating effective and efficient Data Visualizations of the summary of an analysis to be presented to stakeholders and management teams on a daily, monthly or yearly basis, continuous testing, and improvement of machine learning models, etc.
Data Engineer
The skill sets required by a Data Engineer comprise the following: Data Warehousing and ETL, Hadoop-based analytics, in-depth knowledge of SQL/databases, data architecture and pipelining, advanced programming knowledge, concepts of machine learning, management and organizational skills, ability to work with cross-functional teams.
Data Scientist
The skill sets required by a Data Scientist include statistical and analytical skills, Machine Learning and Deep Learning principles, Data Mining, data optimization skills, in-depth programming knowledge, Hadoop-based Analytics, good decision making, communication skills, etc.
Data Engineer
Data engineers generally work with cloud databases and data warehouses. They won’t be using data science and machine learning tools. With the ELT approach becoming ever more popular in data engineering, Data engineer use the ELT tools too. Some examples are Hevo, Kafka, Talend, etc.
Data Scientist
Data scientists usually work with large amounts of data. They work a lot with relational (e.g., MS SQL Server, PostgreSQL, MySQL) and NoSQL (e.g., MongoDB, Cassandra, CouchBase) databases and cloud-based data-warehouses such as Snowflake or HIVE. Examples of such cloud databases are Amazon Web Service, Microsoft Azure, and Google Cloud. Data scientists use data science and machine learning tools such as Jupyter Notebooks, MATLAB, KNIME, MS Azure-learning Studio, IBM Watson Machine Learning, etc.
5) Data Engineer vs Data Scientist: Courses and Certification
Data Engineer
Along with the above-mentioned skill sets and Tools knowledge, some certifications can be helpful in your journey of becoming a data engineer. These certifications are more oriented to data engineering specifically, such as:
- Google Professional Data Engineer
- Cloudera Certified Professional (CCP): Data Engineer
- IBM Certified Data Engineer – Big Data
- SAS Certified Big Data Professional
- Data Science Council of America (DASCA) Associate Big Data Engineer
Data Scientist
Along with the above-mentioned skill sets and Tools knowledge, some certifications can be helpful in your journey of becoming a data scientist. These certifications are more oriented to data science specifically, such as:
- IBM Data Science Professional Certificate
- Microsoft Certified: Azure Data Scientist Associate
- SAS Certified Data Scientist
- SAS Certified AI & Machine Learning Professional
- Dell EMC Data Science Track (EMCDS)
6) Data Engineer vs Data Scientist: Programming Languages
Data Engineer
The Data Engineer should be exposed to the following programming languages as they will be used in creating an efficient data pipeline: Java, SQL, SAS, Python, C++, Scala and should be able to handle frameworks like Hadoop, SAP, Pig, Oracle, MapReduce, MongoDB, MySQL, NoSQL, Hive, Sqoop, etc.
Data Scientist
Coding is important for the Data Scientist role hence, the following programming languages are often used: Java, SPSS, SQL, R, Python, SAS, Scala, Julia, and C as well as frameworks such as Hadoop, Pig, Spark, MATLAB, and knowledge of Deep Learning, Machine Learning, etc.
7) Data Engineer vs Data Scientist: Educational Background
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Data Engineer
Most companies hiring Data Engineers look for those with a computer science, computer engineering, applied mathematics, or information technology background. Data Engineers may also acquire some engineering certificates like Google’s Professional Data Engineer or IBM Certified Data Engineer to boost their chances of being picked.
A master’s degree and years of professional experience are an advantage for Data Engineering roles in big corporations.
Data Scientist
Most Data Scientists are usually from a mathematics and statistics background with advanced degrees in these fields, though a lot are also from computer science and engineering backgrounds too.
It is critical to know the fundamentals of computer science and programming to be outstanding as a Data Scientist and having a higher degree such as a master’s or Ph.D. is an advantage.
8) Data Engineer vs Data Scientist: Salary and Job Openings
Data Engineer
Both Data Engineer and Data Scientist positions offer a highly rewarding career as it is very lucrative. The annual average salary of a Data Engineer is about $142,000 per year depending on the company and your job description.
Data Scientist
As with the Data Engineer, the salary of a Data Scientist is attractive and has an average of $132,000 per year depending on your skills and qualification. As for the job outlook and opportunities, the world is producing lots of data that needs to be fused and analyzed. Hence, the job roles for both Data Engineers and Data Scientists are readily available and on the increase as demand for cleaned and sophisticated data is high.
9) Data Engineer vs Data Scientist: Career Path
Data Engineer
When it comes to the Data engineer’s carer path has a unique set of opportunities where you can move from being a data engineer to a data scientist. Below image shows the career opportunities for data engineers:
Data Scientist
Below image shows what a typical data scientist career ladder looks like:
Data Engineer vs Data Scientist: Summary
| Parameter | Data Engineer | Data Scientist |
| Roles | Data Engineers are saddled with the responsibility of establishing the foundation on which Data Analysts and scientists build. They are tasked with designing, building, integrating, testing, managing, and optimizing data from various sources. | Data Scientists are usually the ones that provide insights into data by conducting high-level research and analysis on the data to identify trends and opportunities that will help the growth of the enterprise. |
| Responsibilities | Building APIs for data connection, creating avenues for the integration of external and new datasets into existing data pipelines by building complex queries, monitoring | Desing statistical models to evaluate and validate the data for analysis, integrate data and perform ad-hoc analysis, build predictive models by using machine learning algorithms, creating effective and efficient Data Visualizations of the summary of an analysis to be presented to stakeholders and management teams on a daily, monthly or yearly basis, continuous testing, and improvement of machine learning models, etc. |
| Required Skill Sets | The skill sets required by a Data Engineer include Data Warehousing and ETL, Hadoop-based analytics, in-depth knowledge of SQL/databases, data architecture and pipelining, advanced programming knowledge, concepts of machine learning, management, and organizational skills, ability to work with cross-functional teams. | The skill sets required by a Data Scientist include statistical and analytical skills, Machine Learning and Deep Learning principles, Data Mining, data optimization skills, in-depth programming knowledge, Hadoop-based Analytics, good decision making, communication skills, etc. |
| Tools | Data engineers generally work with cloud databases and data warehouses. They won’t be using data science and machine learning tools. With the ELT approach becoming ever more popular in data engineering, Data engineer use the ELT tools too. Some examples are Hevo, Kafka, Talend, etc.
| Data scientists use data science and machine learning tools such as Jupyter Notebooks, MATLAB, KNIME, MS Azure-learning Studio, IBM Watson Machine Learning, etc. |
| Courses and Certifications | 1) Google Professional Data Engineer
2) Cloudera Certified Professional (CCP): Data Engineer
3) IBM Certified Data Engineer – Big Data
4) SAS Certified Big Data Professional
5) Data Science Council of America (DASCA) Associate Big Data Engineer | 1) IBM Data Science Professional Certificate
2) Microsoft Certified: Azure Data Scientist Associate
3) SAS Certified Data Scientist
4)SAS Certified AI & Machine Learning Professional
5) Dell EMC Data Science Track (EMCDS) |
| Programming Languages | The Data Engineer should be exposed to the following programming languages as they will be used in creating an efficient data pipeline: Java, SQL, SAS, Python, C++, Scala and should be able to handle frameworks like Hadoop, SAP, Pig, Oracle, MapReduce, MongoDB, MySQL, NoSQL, Hive, Sqoop, etc. | Coding is important for the Data Scientist role hence, the following programming languages are often used: Java, SPSS, SQL, R, Python, SAS, Scala, Julia, and C as well as frameworks such as Hadoop, Pig, Spark, MATLAB, and knowledge of Deep Learning, Machine Learning, etc. |
| Educational Background | Most companies hiring Data Engineers look for those with a computer science, computer engineering, applied mathematics, or information technology background. | Most Data Scientists are usually from a mathematics and statistics background with advanced degrees in these fields, though a lot are also from computer science and engineering backgrounds too. |
| Salary and Job Openings | The annual average salary of a Data Engineer is about $142,000 per year depending on the company and your job description. | The salary of a Data Scientist is attractive and has an average of $132,000 per year depending on your skills and qualification. As for the job outlook and opportunities, the world is producing lots of data that needs to be fused and analyzed. |
| Career Path | Typically a career path for a Data engineer involves starting from entry-level data engineer, senior data engineer, and data engineer manager. Upskilling and gaining experience results in arising of opportunities to change jobs from data engineer to data scientist. | The career journey for a Data Scientist starts with an entry-level Data Scientist, Senior Data Scientist, and Data Science Director. |
Conclusion
This article gave a comprehensive analysis of the 2 popular job roles in the Data Analytics field today: Data Engineer and Data Scientist. It also provides a brief overview of both designations. It also gave the parameters to judge each of the roles. Overall, the Data Engineer vs Data Scientist choice solely depends on your skillset and your ability to analyze data.
Both roles are equally challenging and are vital in the Data Analytics field today. Although there are a few differences in salary among both roles, each has its own importance in this field. Data Engineers are responsible for maintaining and managing Databases and Data Warehouses and Data Scientists are important to analyze, test and optimize that data in order to gain valuable insights from your customers.
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