Are you interested in data science and engineering? What is data engineering? Data engineering is a piece of good news for engineers who are intrigued by data science. Data science is presently a very lucrative career option. From 2022 to 2032, the demand for data science professionals is expected to increase by 35%, which is much faster than the average growth rate for all professions.
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So, let us explore the important facts about data-driven science and engineering and learn how to become a data engineer.
Here’s what we’ll cover in this article:
- What is Data Engineering?
- What Does a Data Engineer Do?
- Perks of Becoming a Data Engineer
- How to Become a Data Engineer?
- Get Interview-Ready For Data Science Engineering with Interview Kickstart
- FAQs About Data Engineering
What is Data Engineering?
The discipline of data engineering includes methods for putting together, storing, and estimating vast amounts of data. It has applications in practically every industry and covers a wide range of scenarios. In recent years, the IT field's most in-demand expertise has been data engineering. Organizations can collect huge amounts of data. So, to ensure that information is in a highly usable state when it reaches data scientists and analysts, they need the appropriate processes and expertise.
The availability and quality of data must be organized and ensured in order for data scientists to have access to it in a secure manner. Data Engineers create the groundwork for successful Data Science efforts.
What Does a Data Engineer Do?
An IT employee who prepares data for analytical or operational purposes is recognized as a data engineer. They want to make data available so that businesses may assess and improve performance. They create data pipelines and combine, clean, and organize the data for analytics applications.
The workload of data engineers varies with the size of the organization; larger businesses typically need more complicated analytics systems and data, particularly in sectors like healthcare, retail, and financial services. Data engineers are highly regarded since they help businesses succeed. Data engineers help data scientists to promote data transparency. This teaming up of two different professionals lets businesses prosper through better decision making. Working as a data engineer can help to improve the lives of data scientists while also allowing you to have a real impact.
By 2025, data engineers will be producing 463 exabytes each day. That is one byte followed by 18 zeros of data. Without data engineers processing and directing the data, fields like machine learning and deep learning cannot prosper.
Data Engineer Roles
A data engineering career builds along various roles:
- Generalist Data Engineer: A data-focused individual who processes data for analysis within small teams.
- Pipeline-centric Data Engineer: Handles more complex data needs, collaborating with Data Scientists using Data Engineering methods, requiring computer science and distributed systems knowledge for effective analysis.
- Database-centric Data Engineer: Sets up and generates analytics databases and engages in pipeline tuning, quick analysis, and schema design for larger organizations with dispersed data across multiple databases.
Data Engineer vs. Data Scientist
Data Engineer and Data Scientist are two separate positions within the discipline of data science. Both positions work together but have distinct roles in data workflow and analytics.
Perks of Becoming a Data Engineer
Apart from being challenging, a data-driven career as a data engineer can be very rewarding. You will be a crucial component in the success of an organization as you provide data scientists, analysts, and decision-makers with the necessary information they need to perform their duties. Scalable solutions are only possible with a data engineer’s ability to program and address issues.
- Career Path of Data Engineer
Data engineering is rarely an entry-level position. As opposed to this, a lot of data engineers begin their careers as software engineers or business intelligence analysts. As they grow, they might take on administrative responsibilities or turn into a chief data officer, data architect, or data scientist.
- The Compensation for Data Engineers
Due to the high level of technical competence required and the requirement for advanced training, data engineering is a well-paying profession. In the United States, a data engineer makes an average yearly pay of $123,321. This is a LinkedIn report after the manipulation of 7.5k salaries, updated on September 26, 2023. Also, there are many noncash benefits of around 401(k).
Note: Location, experience, company size, and job responsibilities all affect a person’s salary.
How to Become a Data Engineer?
For a data engineering job, experience is also required to be considered for a job position. The various ways to crack into a data engineering career include the following options:
- College Degrees: Graduation degrees in computer science, physics, applied mathematics, or engineering are helpful. Moreover, obtaining a post-graduation degree in computer science or computer engineering is advantageous.
- Online Courses: Data Engineering skills can be learned through some trusted, affordable and free courses on online platforms are a good way to upskill.
- Project-based Learning: The project-based method of learning data engineering skills entails establishing project objectives and identifying the abilities required, as well as motivating students and organizing their learning.
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FAQs About Data Engineering
Q1. What is the difference between a data analyst and a data engineer?
Data analysts have the responsibility to analyze the data sets to understand the insights. Data engineers create systems that collect, validate, and prepare high-quality data. Data scientists use the data to support better business decisions, while data engineers collect and process the data.
Q2. Is data science a growing industry?
The big data and technology sectors are expanding rapidly, leading to a 35% increase in data science employment between 2022 and 2032, with an average of 17,700 job opportunities for data scientists over the next ten years.
Q3. What skills should I learn for data engineering?
There are a number of skills to learn for data engineering. SQL, NoSQL, Python, Amazon Web Services (AWS), ETL tools, Kafka, Hadoop, clear & concise writing, interpersonal communication, and time management are some of the key skills.
Q4. Can I clear a data engineer interview?
Data engineering is a fast-growing career with incredible rewards and challenges. There are many rewards, as discussed above. When talking about challenges, interview clearance is one of them. Interviewers seek data engineers' motivations and skills. You need to demonstrate a selective range of skills in order to succeed in the data engineering interview.
You must take your time and successfully display your skills in problem-solving and analysis when responding to each question. Your responses should demonstrate your capacity for critical thought and a sensible mentality. It is always wise to join a promising online course that can make you interview-ready.
Q5. What is the highest data engineering salary at Amazon?
Amazon has offered the highest salary package for a Data Engineer. The total annual compensation of $300,000. This includes a base salary as well as any potential stock compensation and bonuses.