How to Create an Impressive Data Engineering Portfolio as a College Student?

Data engineering portfolio needs relevant effort to showcase the necessary skills and knowledge

There is a huge demand for different types of data professionals in this global data-centric market. Data engineer is one of the data professionals that represent the confluence between software engineering as well as data science. This data science job needs an impressive data engineering portfolio that includes soft skills and hard skills. Data engineering portfolio can land a lucrative salary package from eminent companies working with data. Data engineering is one of the hottest data science jobs in the current tech market. Aspiring data professionals or data engineer often need to have a deep understanding of creating a good data engineering portfolio to showcase necessary skills and knowledge. Thus, let's explore some crucial steps for making the data engineering portfolio valuable for tech companies to hire talented data engineers in 2022.

Key elements to create an impressive data engineering portfolio

Strong understanding of the data engineering field and concepts

A data engineer should showcase a strong understanding of the data engineering domain concepts. This may include performing multiple activities such as acquiring datasets for aligning with business needs, developing algorithms, creating new data validation methods, ensuring compliance with data governance, and many more. This data science job also includes managing data warehouses and tracking necessary data as a data engineer.

Developing data engineering skills

Data engineers must show the development of data engineering skills to create an impressive data engineering portfolio. Aspiring data professionals need to learn the fundamentals of cloud computing, data management, relational and non-relational database design, coding, ETL, automation, machine learning, data security, and many more. Reliable data engineering skills are needed in data engineering to land a secured data science job in reputed tech and data-driven companies.

Completion of certification courses

A data engineer must look out for different certification courses on multiple educational platforms or companies like Google Data Analytics or IBM to create an impressive and valuable data engineering portfolio. Completion of certification courses can make tech companies believe that data professionals know the concepts of the data science job and can manage the roles and responsibilities effectively and efficiently. Certification courses are an excellent way to improve skills and knowledge of being a data engineer.

Working on data engineering projects

A data engineer must work on multiple data engineering projects to showcase the understanding of necessary skills for this data science job. Data professionals should add relevant and valuable data engineering projects by posting the link to their LinkedIn profile or GitHub.

Join an entry-level position

It is essential for data professionals to join an entry-level position as data engineer to gain hands-on experience, brush up on the necessary skills, and become qualified for being a professional data engineers. An entry-level position is often helpful to start creating an impressive data engineering portfolio to earn a lucrative salary package per annum in the nearby future.

Completion of a Master's/Ph.D. degree

Yes, some of the data engineer job profiles need only a Bachelor's degree in any technical field from any UGC- recognized university across India. But it will be useful if an aspiring data engineer completes the Master's degree or a Ph.D. It can be completed in subjects such as Computer Science, Data Science, Software Engineering, and many more. The curriculum includes multiple optional modules in databases as well as advanced data techniques.

Thus, these steps are just a few important keys to creating an impressive data engineering portfolio to get recruited for this data science job. All kinds of data professionals can follow these steps and this is not only for data engineers.