In data engineering, systems are designed and built for collecting, storing, and analyzing data. Almost all industries are involved. It is a wide-ranging field. Massive amounts of data may be collected by organizations, but there are also barriers to ensuring that the data is in a highly usable state after it reaches the analysts and data scientists. Data engineers are part of a growing field where we’ll be producing 463 exabytes a day by 2025, so on top of making the lives of data scientists easier, they can make a tangible difference in the world. The amount of data is one byte and eighteen zeros. Data engineers are critical to the success of fields such as machine learning and deep learning.
What does a data engineer do?
The job of a data engineer consists of creating software that collects, manages, and transforms raw data into reports for data scientists and business analysts. They are helping companies make data more accessible so that they can optimize their performance using it. You can learn more about what data engineers do by listening to some real life examples.
The following are some common things you might do asks you might perform when working with data: Building, testing, and maintaining database pipeline architectures Partner with management to understand company objectives Craft new data validation methods and data analysis tools Ensure compliance with data governance and security policies In a smaller company, generalists typically take on more tasks related to their company’s data. Several large companies employ data engineers who specialize in developing pipelines for data analysis, while others manage data warehouses, creating database schemas to track data and populating warehouses with data.
Why pursue a career in data engineering?
The field of medicine offers rewarding career prospects as well as challenges. Data scientists, analysts, and decision makers require easy access to data in order to be effective, which you will do as an experts in the field. In order to build scalable solutions, you’ll have to rely on your programming and problem-solving skills. It is clear that data engineers will remain in high demand for as long as there is data to process. According to Dice Insights, data engineers beat out computer scientists, web designers, and database architects  as the top trending jobs in 2019. In 2021, LinkedIn identified it as a job on the rise.
Data engineer salary
Besides being a well-paying career, data engineering can also be a rewarding one. US data engineers make a median salary of $111,933, varying from $164,000 to $164,963 per year. In terms of pay, data engineers are well compensated for their skills, especially in comparison to other data-focused roles such as data analyst ($68,0000) and database administrator ($81,444).
Data engineer career path
It’s not always the case that data engineers are entry-level positions. As a result, many data engineers start their careers as software engineers. Your career may also lead you to managerial positions, solution architects, machine learning engineers, or data architects.
How to become a data engineer
Data engineering can offer many rewarding career opportunities for individuals who possess the right skills and knowledge. An individual working as a data engineer typically holds a bachelor’s degree in computer science. Gain the knowledge you’ll need to excel in this rapidly evolving field with a degree. With a master’s degree, you may have the chance to further your career and enter positions that can pay more. There are a number of other ways to increase your chances of success besides earning a degree.
1. Develop your data engineering skills.
Get a head start on a career in data science by studying cloud computing, coding, and database design. Code: : If you are interested in this position, then you should consider taking courses to improve your skills in coding languages. Python, Java, R, and Scala are some of the most common programming languages. Databases are divided into two types: relational and non-relational. For storing data, databases are one of the most popular options. Understanding how databases work, both relational ones and non-relational ones, is essential. Extract, transform, and load (ETL) ract, transform, and load) systems: Data is moved from different data sources into a single repository, such as a data warehouse, with the help of ETL. Alooma, Talend, and Xplenty are some of the most popular ETL tools. Storage of data: : Particularly with respect to big data, it is not recommended to store each type of data the same way. Whether to use a data lake or a data warehouse will depend on how you are designing your data solutions for the company. A programming language that automates tasks. Having big data at your fingertips makes automation vital for organizations due to their ability to gather so much data. If you want to automate repetitive tasks, then you should be able to write scripts. Learning by machine: : Data scientists are normally the ones concerned with machine learning, but having a solid understanding of the basics can help you better comprehend the needs of the data scientists. Tools for big data: : In the data engineering field, data isn’t just information. The task of managing big data is often assigned to them. A few of the most popular tools and technologies are Hadoop, MongoDB, and Kafka, which are constantly evolving. Computing in the cloud. The use of cloud services is on the rise as organizations move toward trading physical servers for cloud storage and computing. An introduction to Amazon Web Services (AWS) or Google Cloud is beneficial for beginners. Security of data: : The task of data managers and engineers is still to secure the data they oversee to prevent it from being lost or stolen, even when some companies have dedicated data security teams.
2. Get certified.
The certification of your skills can show potential employers that you have the required knowledge and skills. Associate Big Data Engineer, Cloudera Certified Professional Data Engineer, IBM Certified Data Engineer, and Google Cloud Certified Professional Data Engineer are all options available. Find out what job opportunities are available so that you can apply. You might want to consider starting by looking at a certification that is often suggested as required.
3. Build a portfolio of data engineering projects.
It is often vital to present your portfolio to recruiters, hiring managers, and potential employers in order to be considered for the job. A portfolio website (that uses a service like Wix or Squarespace) is a great way to showcase data engineering projects you’ve completed independently or as part of coursework. You can also submit your work to a site like GitHub, or to the Projects section of your LinkedIn profile – both free alternatives to a standalone portfolio site. Brush up on your skills with a portfolio-ready guided project and complete it in under an hour without any software.
4. Start with an entry-level position.
As a young professional, there are many data engineers starting out as analysts or database administrators. Your skills can develop as you gain experience, and you may qualify for more advanced roles as you progress. Learn about this Data Engineering Career Learning Path from Coursera as an example of a possible learning path.
If you want to switch careers or get a head start in your career, earn a Google Data Analytics Professional Certificate, IBM Data Science Certificate, or IBM Data Engineering Certificate.