What Skills Should Data Engineers Have?
Data Engineers must know how to create reliable data pipelines, architect distributed systems, combine data sources, write queries, wrangle data, optimize systems, engineer for scale, and architect data stores. A strong foundational understanding of the extract, load, transform methodologies (ELT) is also expected. Collaborating with data science teams to build the right solutions for them will require strong communication skills with both non-technical and data-skilled staff members.
Tools, like Hadoop, Spark, Kafka, and Hive, will be useful for a Data Engineer. Coding is a large part of the role of Data Engineer; they must know SQL at a minimum and might find learning more languages, such as Scala, R, and Python, valuable, as each company will have their own preferred coding languages. Data Engineers should also know the major database systems that companies use, such as MySQL, MongoDB, Postgres, Cassandra, and Oracle. Some companies are converting to cloud-based services and some Data Engineers will need to know cloud infrastructure best practices for services such as Amazon Web Services (AWS), Google Cloud, or Microsoft Azure. Not all Data Engineers will need to know these skills.
It is important for a Data Engineer to have strong foundational knowledge in a wide variety of tools to rely upon when choosing the proper technology for their company’s needs. Tools can be and often are, learned on the job, while concepts are something a Data Engineering candidate must be proficient in before applying for anything.
Learn the Skills You Need to Become a Data Engineer
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Data Science
Data science combines domain knowledge, programming skills, mathematics, and statistics to infer crucial insights from data. These insights can be used by businesses, governments, and any other data-collecting entities to inform decisions.
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Python
Python is an interpreted, object-oriented, high-level programming language with dynamic semantics. It is used to write scripts, automations, algorithms, manipulate data, and create frameworks. Python prioritizes simplicity, easy to learn syntax, readability, and versatility.
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SQL
SQL stands for Structured Query Language. It is a computer language used to store, manipulate, and retrieve data which is stored in a relational database.
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Machine Learning
Machine learning is the use and study of computer algorithms that improve automatically through experience. It is a subset of artificial intelligence (AI). Machine learning is used in everything from email filtering to Netflix recommendations.
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R Programming
R is a programming language and free integrated development environment (IDE) for statistical computing and graphics. R is most commonly used by Data Scientist and Statisticians for developing statistical software and data analysis.
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Mathematics
Mathematics are used on a day-to-day basis by many technical positions. Subjects like linear algebra, calculus, statistics, and probability are used by Data Scientists, Cybersecurity professionals, Developers, Motion Graphics Designers, Engineers, and more.