Through the right set of skills and knowledge, you can launch or develop a rewarding career in data engineering. Many data engineers have a bachelor’s degree in computer science or in a related field. By obtaining a degree, you can build the foundation of knowledge you will need in this rapidly evolving field. Consider a master’s degree to get a chance to advance in your career and open positions that may be of higher wages.
Besides obtaining a degree, there are many other steps that you can take to prepare yourself for success.
1. Develop your data engineering skills
Learn cloud computing fundamentals, coding skills and database design as a starting point for a career in data science.
• Coding: Proficiency in coding languages is essential for this role, so consider taking courses to learn and practice your skills. Common programming languages include SQL, NoSQL, Python, Java, R, and Scala.
• Relational and non-relational databases: Databases are among the most common storage solutions. You must know what relational and non-relational databases are, and how they work.
• ETL systems (extract, transform, and load): ETL is the process by which you will move data from databases and other sources into a single repository, such as a data warehouse. Common ETL tools include Xplenty, Talend, Stitch, Alooma etc.
• Data storage: Not all data types should be stored in the same way. especially when it comes to big data. When designing data solutions for a company, you need to know when is the best time to use a data lake and when to use a data warehouse, for example.
• Automation and scripting: Automation is a necessary part of handling big data because organizations are able to collect so much information. You must write scripts for repetitive tasks to ease on your work.
• Machine Learning: While machine learning is more of a data scientist focus, mastering the basic concepts can help you better understand the needs of data scientists on your team.
• Big Data Tools: Data engineers don’t work with regular data. They are usually responsible for managing big data. Tools and techniques are constantly evolving and vary from company to company, but some popular ones include Hadoop, MongoDB, and Kafka.
• Cloud Computing: As companies increasingly trade physical servers for cloud services, you need to understand cloud storage and cloud computing.
• Data security: Although some companies may have dedicated data security teams, many data engineers are still responsible for safely managing and storing data to protect them from loss or theft.
2. Get certified.
Certification allows you to test your skills against potential employers, and preparing for a certification exam is a great way to develop your skills and knowledge. Options include Associate Big Data Engineer, Board infinity data Science, Cloudera Certified Professional Data Engineer, IBM Certified Data Engineer, or Google Cloud Certified Professional Data Engineer.
3. Build a portfolio of data engineering projects.
The portfolio is an important component of job search because it shows what recruiters, hiring managers, and potential employers can do.
You can add completed data engineering projects individually or as part of your coursework to your portfolio website (using services such as Wix and Squarespace). Otherwise, post your work on a site such as the Projects section of LinkedIn profile or GitHub, both are free alternatives to standalone portfolio sites.
Develop your big data skills on a guided tour of your portfolio and complete them in a short time.
4. Start with an entry-level position.
Many data engineers begin to perform entry-level functions, such as a business analyst or database administrator. Having gained experience, you can acquire new skills and apply for more advanced roles.