According to the CompTIA 2023 State of the Tech Workforce report, “Data Engineering job roles are growing over the next 10 years at twice the rate of overall employment across theeconomy.”
Data engineersaremore in demand than data scientists, and the need is still growing.
Today businesses run on data which gets converted into knowledge. Data usagecontributes togrowing strategic decisioncapabilities.
As long as businesses require “data management”, the demand for data engineers persists.
What is Data Engineering?
Data engineering is building tech-efficient teams to collect, store and analyze data.The process involves building systems and teams to collect raw data from multiple sources and formats.
Similarly, data engineers are responsible for channelizing data and working in various settings.
The work system involves tech people to define the practical applications of data and answer critical business questions.
Data engineers are supporters of the data-finding process. Their job is to help the consumers of data (data analysts, data scientists) securely use their findings to answer queries quickly and efficiently.
In short,data engineering includes tasks like acquisition, cleansing, conversion, disambiguation, and deduplication. Once the task gets complete, data engineers store data in a central repository (also known as a data lakehouse) for future use.
The present industry value of data engineers
The data engineering sector is hot for tech enthusiasts like you.
In a research Zippia’s data science team projected job growth for data engineers is 21% from 2018-2028.
The statistics clearly highlight the increasing demand for data engineers.
Wondering what makes this job profile in high demand?
Data engineers are in demand due to the growing reliance on data-driven decision-making. The complexity of data also developed an alternative to collect and analyze data.
Organizations prioritize designing, building, and maintaining a perfect data infrastructure for business growth. Tech leaders are looking for certified data professionals who can help them grow.
As a result, data engineers often receive competitive salaries and benefits. The specific value can vary depending on factors such as location, experience, and the organization’s size and industry.
How to become a data engineer?
To take advantage of the professional opportunities within data engineering, staying relevant in the field is important.
Data engineer veteran David Bianco says, “Languages come and go, but concepts behind building a robust pipeline stay.”
Here are a few guidelines to follow to build a career in data engineering:
- Gain experience in data engineering: Working on personal projects, internships, or entry-level data engineering roles helps fine-tune the key data concepts.Job experience enables you to master TSQL, Python, Tableau, Snowflake, TFS, Azure,and DevOps. Working on LIVE projects gives youhands on the pulse of this trending field. Also, you can network with industry experts and receive reliable career advice and mentorship.
- Earn a bachelor’s degree in computer science, software engineering, or data science: A bachelor’s degree gives you the foundation of knowledge to better understand the core subject matter. During this duration, you can learn all about data structures, algorithms, data engineering principles, and the tools and technologies used in the field.
- Get data engineering certification: Certification demonstrates your skills and knowledge to potential employers.Data engineering is constantly evolving. Professional certification helpsyou stay up-to-date on the latest data trends. You can do this by applying for online Data Analytics & Engineering Bootcampfrom IT training providers.
Based on theGlassdoor (May 2023) report, the average US salary for data engineers is$97,158 per year.The advanced professionals have a salary pay scale of$116,423/yr.
Data engineering is certainly worth it if you are willing to play an important role in the organization’s growth.
Data Engineer V/s Data Scientist: The prime difference
The field of data engineering has not matured yet and is often confused with data science. When you are in the tech field, clearing doubts always helps. Here is the difference between
DataEngineers V/s Data Scientists.
Data engineers are system builderswith functional areas involving collecting, testing, and preparing high-quality data.
Data scientists are responsible for cleaning, organizing, and analyzing data.
The efforts of both the tech people are to make data usable. But data engineers deal with raw data comprising human and machine errors. At the same time, data scientists use data that has passed the first round of the cleaning process.
Key Takeaways
In today’s competitive job environment, it’s essential to keep upskilling your capabilities. Adding advanced certifications to your resume is a great way to show employers you’re committed to your craft and have the chops to do the job.
Data Analytics & Engineering Bootcamp, Snowflake Data Engineer Training, and Data Analytics Bootcamp are a few industry-standard credentials employers recognize and look for in this tech space.