This is a key question that bugs many data engineering aspirants. In this article, we will try and answer this question as succinctly as possible. Stay tuned.
First, what is data engineering? Data engineering deals with the design and building of pipelines that transform and transport data into a highly usable format for data scientists to work on. These pipelines take data from many a variety of sources and store them as a single source data.
Data engineers develop, test, and maintain information architectures. A data engineer, using his programming skills, Machine learning, and statistical models and is also involved in building pipelines for various ETL operations
A data engineer is expected to have a good understanding of programming in order to design algorithms. A data engineer should also have a high level of competence in statistics and mathematics.
Role of Data engineering in businesses
Data engineering is an in-demand skill required by businesses today. Data engineers design a system that unifies data and makes it possible to navigate it hassle-free. Some of the tasks data engineers do for businesses are:
● Data acquisition: It's the process of finding all the different data sets around the business
● Cleaning data: Finding and cleaning any errors in the data
● Unified formatting: Giving all the data a common format
● Disambiguation: Interpreting data that could be interpreted in multiple ways
● Deduplication: Removing duplicate copies of data
After these steps are completed, data is stored in a central repository such as a data lake or data lakehouse.
Data Engineering Tools and Skills
Data engineers use many different tools to work with data. They use a specialized skill set to create end-to-end data pipelines that move data from source systems to target destinations.
Data engineers work with a variety of tools and technologies, including:
● ETL Tools: ETL (extract, transform, load) tools move data between systems. They access data, then apply rules to “transform” the data through steps that make it more suitable for analysis.
● SQL: Structured Query Language (SQL) is the standard language for querying relational databases.
● Python: Python is a general programming language. Data engineers may choose to use Python for ETL tasks.
● Cloud Data Storage: Including Amazon S3, Azure Data Lake Storage (ADLS), Google Cloud Storage, etc.
● Query Engines: Engines run queries against data to return answers. Data engineers may work with engines like Spark, Flink, and others.
Let's now have a look at the scope of data engineering as a career choice:
Future scope of Data engineering:
A career in the data engineering field is rewarding as well as full of challenges. A lot of responsibility rests on a data engineer. Data engineers help companies with easy access to data that can be used by data scientists to derive actionable insights These insights can help the senior management of companies to make better decisions for improving the profitability, process flow, and efficiency.
The demand for data engineers will continue to exist and flourish in the data-driven economy today. As per a study done by Dice Insights in 2019, data engineering scores as the top trending job in the technology industry, surpassing the demand for computer scientists, web designers, and architects. In 2021, LinkedIn listed it as a career that was on the upswing in terms of opportunities in it.
Salary of a Data Engineer
Data engineering commands a handsome salary which continues to rise as the demand for data engineers rises. According to data from Glassdoor, the average salary of a data engineer in the US is $115,176. The highest salary for entry-level data engineers is as much as $168,000 per annum. This is quite high when compared to average compensation in some of the more popular job profiles such as software engineers.
Career progression of a Data Engineer
Contrary to what's usually said, a Data engineering role isn't always an entry-level one. Many data engineers begin their careers as software engineers or business intelligence analysts. As they move ahead in their career, they may branch off into managerial roles or become data architects.
Sometimes, data engineers also branch off into careers such as data scientists, senior data scientists, etc. Hence, in terms of creative potential as well as vertical mobility, data engineers have much to look forward to.
Conclusion
In this article, we have discussed the future career prospects in the data engineering field. As the digital age surges on, more businesses and institutions are expected to expand their digital footprints. This will only lead to a further rise in the demand for data professionals, including data engineers. Hence, learners aspiring to become data engineers must focus on upskilling to latch onto the most lucrative opportunities in the data engineering career space.
Institutes such as Skillslash have well-designed courses for you to start learning data engineering skills. Skillslash's Full Stack Development Course In Bangalore and Data Science Course In Bangalore help you to not only upskill but also get sufficient project experience and a placement guarantee. Hence, learners may consider browsing through the course for a better understanding of what I mean.
