Before exploring more about these trending professions in our blog on Data Engineer vs. Data Scientist, let’s look at the topics covered in this blog:
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Data Engineer vs. Data Scientist: Roles and Responsibilities
In the comparison of Data Engineer vs. Data Scientist, you need to remember that both roles have their respective responsibilities in the field of data. A Data Engineer handles the operations of the raw data before transferring it to the database, whereas a Data Scientist works on analyzing and interpreting the processed data, using statistical models and machine learning algorithms to extract valuable insights and make data-driven decisions.
Let’s now check out the difference between Data Engineer and Data Scientist roles.
What Does a Data Engineer Do?
Data Engineers are masters of designing, building, testing, integrating, and optimizing the raw data from various sources for operational or analytical purposes. A Data Engineer works on the improvement of data based on reliability, efficiency, availability, and quality. For example, in a car manufacturing firm, a Data Engineer figures out the data that is to be stored in a data schema. Then, he looks for the storage space, also known as Data Warehouse, which is secure, accessible, and reliable. The role performed by a Data Engineer can be divided into 3 categories:
Design: They design the entire data architecture on which the Data Scientists work. They work on structured as well as unstructured data and create the foundation for data scientists to perform analysis and interpretation.
Build: Data Engineers build data pipelines by collecting data from various sources. They are responsible for converting the data into a usable format and implementing and maintaining analytics databases.
Arrange: Data Engineers organize the data so that it can be used in specific analytics applications. Also, they are responsible for performing data cleaning and consolidation.
What Does a Data Scientist Do?
After getting the processed data, the role of a Data Scientist comes into play. The Data Scientist uses techniques like clustering, decision trees, and neural networks to uncover valuable insights. This enhances decision-making, identifies trends, discovers opportunities, and provides a deep understanding of customers and areas for improvement. The role of a Data Scientist is explained in the following steps:
Analyze: Data scientists perform analysis on large sets of complex data to recommend the right course of action for significant solutions to business problems.
Test: They are also involved in software testing whether the applications or models are meeting the business needs and requirements. They monitor the performance of the models to identify whether the goal is being met or not.
Derive Insights: Data Scientists make sense of the data by identifying trends and hidden patterns with the business goals or problems. These derived insights are used by the management for a data-driven business decision-making process.
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Data Engineer vs. Data Scientist: Key Differences
Refer to the table below to learn about the key differences between a data engineer and a data scientist.
| Key Factors | Data Engineer | Data Scientist |
| Role | Handles operations on raw data, and also, prepares and manages data pipelines for storage and processing | Analyzes and interprets data, as well as builds models using statistical and machine learning algorithms |
| Responsibility | Develops, designs, tests, and maintains architectures such as databases and large processing systems | Examines data, develops algorithms, creates predictive models, and generates actionable insights |
| Skills | Programming languages like Python or Java, databases like SQL, and NoSQL, and ETL tools like Apache Spark | Statistical analysis, machine learning algorithms, data visualization, and tools like TensorFlow, R, or Scikit-learn |
| Tools | Uses tools like Apache Hadoop, Spark, Kafka, and SQL databases | Utilizes tools such as Python libraries, R, Jupyter Notebook, and Tableau |
| Goal | Focuses on building and maintaining infrastructure for data processing and storage | Concentrates on deriving insights, patterns, and trends from data to drive decision-making and strategy |
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Data Engineer vs. Data Scientist: Education Background
As both job profiles complement each other, there is one thing in common, and that is the educational background. If you want to pursue any of these profiles, you should have a background in computer science.
There used to be a requirement for a dedicated skillset, as both fields required domain expertise. But now-a-days, people with different or non-tech backgrounds can also pursue careers in these fields. All they require is knowledge and expertise.
Education for a Data Engineer
The education of a data engineer typically includes
- A Bachelor’s or Master’s degree in Computer Science, Information Technology, or related fields.
- Common courses include Database Management, Data Structures, Algorithms, Software Engineering, and ETL (Extract, Transform, Load) Processes.
- Knowledge of database technologies and languages such as Python and SQL
Education for a Data Scientist
The education of a data scientist often includes
- A Bachelor’s or Master’s degree in Computer Science, Statistics, Mathematics, Engineering, or related fields.
- Programming languages like Python or R
- Knowledge of Statistics, Machine Learning, Deep Learning, Data Mining, Data Visualization, and Predictive Modeling.
- Additional skills may involve a strong understanding of statistical methods, artificial intelligence, data preprocessing techniques, and proficiency in data visualization libraries like Matplotlib and Seaborn.
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Various skills, tools, and languages are considered weapons for both Data Engineers and Data Scientists. However, there may be some differences here as well. Next, let’s explore the tools, languages, and skills of these professionals.
| Data Engineer | Data Scientist |
| Oracle | Tableau |
| SAP | RapidMiner |
| Cassandra | MATLAB |
| Redis | Excel |
| Sqoop | PowerBI |
| MySQL | SAS |
| PostgreSQL | Apache Spark |
| Riak | TensorFlow |
| Hadoop | Jupyter |
| Neo4j | Seaborn |
| Hive | PyTorch |
Languages
As coding plays an important role in implementing systems, Data Engineers and Data Scientists, both should be proficient in certain programming languages. Let’s further check out the best languages used by them.
| Data Engineer | Data Scientist |
| Python | Python |
| Java | R |
| C++ | Java |
| Scala | MATLAB |
| SQL | C |
| SQL |
| Scala |
Skills
It is really important to upgrade yourself with the desired skills to be ready to enter this world of competition. Let’s check out the comparison between Data Scientist vs. Data Engineer skills: The Data Engineer profile requires you to have an in-depth understanding of different programming languages, such as SQL, Java, SAS, Python, etc. In addition to that, you should also be a master at handling frameworks such as MapReduce, Hadoop, Pig, Apache Spark, NoSQL, Hive, Data Streaming, and others. You must also have a logical aptitude, organizational and management skills, leadership skills, etc., and you should be a team player who can coordinate with other members and with different teams.
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A Data Scientist is required to have expertise in the fields of mathematics, statistics, and probability, in-depth knowledge of programming languages, such as Python and R, and a good command of visualization and extraction tools. As a Data Scientist, you should possess broad knowledge in the fields of Machine Learning and Deep Learning as they will help you come up with high-value predictions that ultimately lead to better and smarter decision-making. You should also have good communication, management, and presentation skills to present and convey the results of your analysis to higher management and other stakeholders.
Data Engineer vs. Data Scientist: Salaries
As the field of data is growing at an enormous pace, it has created a large space and opportunities for professions related to data. Forbes claims that the Data Engineer and Data Scientist jobs are emerging as top-ranking around the world. Harvard stated that Data Scientist jobs are the top jobs of the 21st century.
Companies such as Facebook, Intel, Microsoft, S&P Global, Schneider, Moody’s, Amazon, etc. are interested in hiring Data Scientists at a great salary package. On the other hand, tech giants, including Google, Apple, Cognizant, Walmart, and others, are offering high-paying jobs to Data Engineers.
Let’s look at the difference between Data Scientist and Data Engineer Salaries based on their salary slabs:
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Below discussed are the salaries earned by both Data Engineers and Data Scientists.
Data Scientist Salary:
Data Engineer Salary:
The highest-paid job, no doubt, is a Data Scientist profile, the Data Scientist’s salary draws between US$4,33,000 and US$9,50,000 per year, with 0–4 years of experience. Whereas, the salary of a Data Engineer lies somewhere between US$116,000 and US$60,000 per year according to Glassdoor. These salary slabs are highly lucrative considering the fact that they are offered at the entry level.
Data Engineer vs. Data Scientist: Which is a better career?
When it comes to the comparison of a Data Engineer and a Data Scientist, you need to keep in mind that both roles have their importance in the field of Analytics. You can also say that the difference between the Data Engineer and Data Scientist roles does not affect their mutual impact on the field of data. Both need each other to achieve the common goal of processing the data in an efficient and business-oriented way.
Ultimately, there are ample opportunities for both Data Engineers and Data Scientists, and this scope is always proportional to the growth of the data. So, upskill yourself to experience an exciting and high-paying career in the field of Data Analytics.
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FAQs
Which position calls for a higher level of processing and data manipulation experience?
Data engineers concentrate on creating systems for large-scale data processing, which calls for a high level of infrastructure and data manipulation knowledge.
In what ways do these jobs work together in an organization?
Data scientists can efficiently access and analyze data thanks to the infrastructure and pipelines that data engineers provide.
Is it possible to move from a data scientist to a data engineer position?
Transitions do occur frequently. Professionals frequently switch between employment in data science and data engineering, and vice versa.
Which position typically interacts with business stakeholders most frequently?
Transforming data insights into workable plans, data scientists frequently interact directly with business stakeholders.
What is the primary role of a data engineer compared to a data scientist?
Data engineers focus on designing, constructing, and maintaining data pipelines and infrastructure to ensure reliable data flow and storage. Data scientists, on the other hand, analyze data to extract insights and build predictive models to solve business problems.
What technical skills are essential for a data engineer versus a data scientist?
Data engineers typically require expertise in database technologies, ETL processes, and programming languages like Python or SQL. Data scientists need strong skills in statistical analysis, machine learning algorithms, and programming languages such as Python or R.
What educational background is common for data engineers versus data scientists?
Data engineers often have degrees in computer science, software engineering, or related fields, with a focus on database systems and software development. Data scientists typically have backgrounds in fields like statistics, mathematics, computer science, or engineering, with expertise in machine learning and data analysis techniques.