Data Analyst vs. Data Engineer: Top Comparison Guide [2024 Updated]
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A career in data science is blooming and has been in high demand in recent years. Companies continuously generate, collect, and analyze big data to run their business effectively with data-driven insights. The two significant aspects of data science are data analysis and data engineering. These data science fields offer better salaries, job positions, benefits, and career growth.
Both cover big data visualization concepts and derive meaningful insights to make informed decisions for business. Therefore, the importance of data analysis and engineering increases. Understanding the critical differences between these roles is essential, and you should be able to comprehend the full potential of both professions. To understand data analyst vs. data engineer, have a look at this top comparative guide.
Data Analysis Overview
Data analysis is a process of analyzing big data and deriving actual patterns from it. Those patterns are further analyzed and used to obtain meaningful insights. These insights are used in business operations, competitors’ strategy building, and obtaining customer information.
Once the company obtains this information, it can easily choose the best course of action for its existing problems. This will lead to greater business revenue and growth. Data analysis is a highly sought-after skill that many companies require.
Who is a Data Analyst?
A data analyst is a professional who analyzes data and performs data analysis techniques to help a company gather meaningful information. They collect and store big data on market research, logistics, and sales numbers. They use their technical ability to maintain data accuracy and quality. With data analysis, facts and trends of data are identified and processed.
The business stakeholders use these insights to make informed decisions for the company. As a data analyst, it is your duty to scrutinize information from different analytical tools like Apache Spark, Tableau, Power BI, and Excel. If you prepare to clear the data analyst certification exam, you can learn about it and its tool through the PL-300: Microsoft Power BI Data Analyst CCSLA prep course.
Data Engineering Overview
Data engineering is also very similar to data analysis but has certain differences. Data engineering is the process of digging and collecting information from data. Data analysts analyze these data, but first, it is the responsibility of data engineers to convert big data sets into ready-to-analyze format.
The data engineering process is more of a practical application-based concept. This concept is slowly getting popular, and today, many companies use it for their functions. For example, automobile companies use this concept in developing automatic vehicles, where self-driving cars make decisions to take turns or read road signs. Data engineering helps recognize traffic and interpret the vehicle’s movement and pedestrians by choosing the appropriate option.
Who is a Data Engineer?
It is important to understand data analyst vs data engineer roles since they are two different professionals. Data engineers’ job is to transform data into ready-to-analyze format. This involves various steps and processes like compilation and installation of database systems, building strategies for disaster recovery management, and writing complex queries. Data engineers create data set processes like mining, acquisition, modeling, and verifications.
To become a data engineer, you must understand better scripting languages and tools like MySQL, MapReduce, Pig, PostgreSQL, and Hive. These tools will help enhance your skills.
However, one can classify the role of a data engineer into three categories, such as:
- Generalists: They are employed in small companies, and they work in data processing and managing big datasets.
- Pipeline-centric: Data engineers work in mid-sized companies and handle complex data sets. They closely monitor data and help data scientists transform it into useful formats.
- Database-focused: Data engineers of this category work in large companies where they manage the flow of data and data warehouses (multiple databases).
Data Analyst vs Data Engineer: Key Comparative Highlights
To understand data analyst and data engineer differences, you must know their individual roles and responsibilities along with the required skillsets, career paths, and salary payout. Let’s discuss all these aspects one by one in this guide.
| Data Analyst | Data Engineer |
|---|---|
| Data analysts work to analyze big datasets and derive meaningful insights from them to help companies make informed decisions. | Data engineers work to prepare data in a relevant format so that data scientists or data analysts can analyze it. They develop, design, test, and maintain data architecture. |
Data Analyst: Roles and Responsibilities
A data analyst does more than analyze data sets. They have specific daily job roles and responsibilities to do.
The following are some significant responsibilities that they have:
- Grow and manage databases to study them and generate insights based on them.
- They collaborate with data scientists and data engineers to derive actionable information.
- They engage in statistical analysis processes to identify patterns in data and visualize them to represent the trend.
- Create reports to communicate with top stakeholders of the business.
- Automates data cleaning and gathering process.
- Generates queries and locates challenges within the framework.
- They ensure proper data acquisition and maintenance.
- Follows data munging, exploratory data analysis, statistics, and data visualization process.
Data Engineer: Roles and Responsibilities
Some essential roles and responsibilities of data engineers are as follows:
- They are involved in preparing data for analytical and operational functions.
- They use programming language tools to do in-depth research on datasets.
- Data engineers develop large data warehouses using ETL (Extract Transform Load).
- Design and maintain database systems and frameworks.
- Integrate different data from multiple sources and optimize their performance.
- They ensure the quality and security of datasets by troubleshooting issues and bugs.
- It’s the data engineer’s duty to support data scientists and analysts with their queries and observations.
Data Analyst: Skills Requirements
Following are some technical skills that every data analyst must have:
- Programming languages: Knowing programming languages is very essential. This helps in analyzing data more efficiently and assists in data handling. If you know the programming language, you can handle the data analysis tool effectively. Some programming languages are R, Java, Python, and SQL, which you should know.
- Data statistical analysis: The data analyst’s primary role is to manage large datasets and draw insights based on that analysis. Optimizing statistical efficiency and quality will be your prime function. Furthermore, you need to use applied mathematical concepts in the analysis process, and thus, statistical analysis skills are necessary.
- Data visualization: As a data analyst, you need to work on various data sets and derive quantitative trends from them to communicate them to other team members. This is where data visualization skills come into action, where you will be required to visualize your observations and share those with the upper management.
- Research skills: Finding accurately and making correct observations is one technical and significant skill that every data analyst should have. This helps eliminate errors in the first step by making accurate observations and deriving better-researched data insights.
Some essential soft skills are:
- Critical thinking
- Analytical ability
- Problem-solving ability
- Better interpersonal skills
Data Engineer: Skills Requirement
Some technical skills that every data engineer should have are as follows:
- Programming language: Data engineers must have coding knowledge. An advanced understanding of Java, Scala, Python, and SQL is essential.
- Database operation systems: A fundamental knowledge of database management systems must be there for every data engineer. As a data engineer, you should know how to navigate different operating systems like Linux, Windows, Solaris, and UNIX.
- Machine learning: Data engineers often deploy high-level languages and deal with data infrastructure frameworks. You must know machine learning concepts and their practical implementations to handle this. Big companies often look for technical skills like machine learning skills in the candidates before they hire.
- Artificial Intelligence: The new trend of AI in the industry has made it essential for candidates to know about AI. Though AI is still growing, you should know its basics and understand how to use it effectively for digging and mining purposes.
Some soft skills you should have are:
- Teamwork spirit
- Analytical mindset
- Focus
- Adaptability
- Creativity
- Problem-solving
Data Analyst: Career Path
To become a professional data analyst, you should master the core skills and get a conventional degree. As a data researcher or analyst, your primary objective will be to collect data and create statistical visualizations based on those data for better insights.
As you grow in your career, you will have responsibilities as a senior data analyst, where drafting data collection processes and streamlining the entire collection into a systematic framework will be your prime objective.
Furthermore, you can specialize in digital marketing, finance, healthcare, or any industry you are interested in to boost your career. You can also become a business analyst and grow in your career with higher positions and salary.
Data Engineer: Career Path
Data engineers usually start as interns in their early career phases. They grow and develop relevant skills in addition to obtaining a degree in software engineering or computer science. As a junior data engineer, you will support the team with your data-building and wrangling ability. Once you build small projects and deal with data architecture delivery, you will get more experience and skills.
For a transition to a senior data engineer post, you can get a relevant certification or, through your network, get placed in higher positions. With better experience, knowledge, and skill, you can even get roles like chief data officer, which comes with great responsibility and a high salary.
Data Analyst vs. Data Engineer: Salary Outlook
The major difference between data analyst and data engineer is in the salaries. The data analyst usually earns from $61,000 to $91,000 per annum. In entry-level jobs, they get handsome earnings, and with their growth and experience, the salary increases.
A data engineer gets a salary ranging from $92,000 to $128,000, which may vary according to their skills, experience, position, and the company they work in. So, the greater the level of expertise and skills, the higher the salary in respect of both data analysts and engineers.
How Do You Start as a Professional Data Analyst?
If you are not aware of how to start your journey as a data analyst, here is what you should follow:
- To get started as a data analyst, you must first have a relevant degree in any data analytical or statistics course.
- You can also get any certifications in data analysis or begin with a baby step.
- Get knowledge of programming languages and in-depth knowledge of SQL and other database systems.
- Acquire the relevant technical and soft skills to crack your interview at entry-level jobs.
- As an entry-level job, you can start with data-building roles, and with experience, you can move to the next stage.
- A data analyst’s professional career begins with specialization in any relevant field, such as business analyst, consultant, senior analyst, financial analyst, or data scientist.
- With experience and skills, your position improves along with the salary. And you get top-level roles like chief data officer, head of analytics, partner, or chief finance officer.
- For a proper boost in your career growth, you can earn a Snowflake Data Analyst Training with CCSLA and get a hike in your salary as well as in job roles.
How Do You Start as a Professional Data Engineer?
To get your journey started as a data engineer, you need to follow specific typical career paths.
Following are some tips through which you can kickstart your journey:
- Get a relevant degree in any software or data-related field like data science or computer science.
- A strong background in statistics contributes a lot to the preparation for higher certification exams and adds weight to the resume or CV.
- Once you have a degree, learn some programming languages like Java, Python, and database SQL.
- Gather basic knowledge of ETL and big data tools. You need to develop certain soft skills along with some essential technical skills to crack interviews for entry-level jobs.
- When you get an entry-level job, your skills and experience will increase. You can learn cloud computing or even get cloud computation training courses like CompTIA Cloud+ for better growth.
- Also, get familiar with operating systems and learn basic machine learning and data visualization techniques.
- You can take some data engineering training to gain practical experience in the industry. Many data engineering bootcamps are available; use them to boost your skills and get a competitive edge in the real world.
Conclusion
Now that we understand how data analysts vs. data engineers differ, you must choose between them based on your interests and personal strengths. If you are good at mathematics and statistical analysis, you can choose data engineering. If you are interested in analyzing big data sets, programming and data visualization, choose data analysis.
Both these roles are highly in demand in the industry and have rewarding career options ahead. Growth opportunities must not be missed to achieve your career goals. If you want to boost your career or even give it a kickstart, you can consider joining a CCSLA bootcamp to get instructor-led training, cutting-edge curriculum, paid internships, and job placement assistance. With CCSLA, you can also get fundamental concepts, integrations, and exam preparation tips.
