Become a Data Engineer: Courses, Salaries, Jobs, & Skills

The Day-to-Day as a Data Engineer

Data Engineers create the infrastructure for data and format data into a useful system that Data Scientists use to analyze large amounts of data. Data Engineers can specialize in pipelines, databases or platforms, warehouses or infrastructure, or be generalists. They work with Data Scientists, upper-level management, Data Analysts, Developers, Data Architects, DevOps Engineers, Database Administrators, and Database Architects. Data Engineers work a typical 40-hour week.

Data Engineers can find work at companies or on projects focusing on artificial intelligence (AI), software, data analytics, healthcare, IT, retail, marketing, government, transportation, science, and more. They can find full-time employment onsite or remotely. Each day is different for a Data Engineer, but you might find them writing queries, creating data pipelines, coding, architecting data stores, combining data sources, or meeting with Data Scientists.

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.

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.

In-person courses Live online courses
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.

In-person courses Live online courses
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.

In-person courses Live online courses
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.

In-person courses Live online courses
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.
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.

Typical Qualifications to Become a Data Engineer

Most employers will expect Data Engineer candidates to have a bachelor’s degree in computer science information technology, or applied math. They should also have some certifications either in database systems such as Oracle, Microsoft SQL Server, IBM, MongoDB, or Apache's Cassandra or something more general such as IBM’s Certified Data Engineer. This role typically demands at least three years of experience working with data.

Tips for Data Engineers

If you’ve never had an IT or programming job before, first try landing a job in a DevOps position, or a job at a startup doing data engineering or programming. These positions will give you the most practical experience and likely have someone on staff who would be willing to mentor you. The most efficient way to learn the most relevant skills quickly is by having hands-on practice with current data and technologies and learning from someone who is in the industry now and has been for a while.

To find a mentor, you can look for jobs at startups, large companies with mentor programs, through an online code mentor service, or through LinkedIn. Join groups on LinkedIn, connect with Data Engineers and start a genuine conversation, or reach out to someone you know through a coding bootcamp or course you’ve taken. You’ll also want to ask your mentor if they’d be willing to do mock-interviews with you, or use an online mock-interview service, as mock-interviews boost confidence and strengthen interview potential. You’ll need to be able to present your technical knowledge and communicate well in multiple rounds of interviews, often over the phone, first.

Data engineering depends on a strong foundation of programming skills and knowledge of industry technologies. Use real-world data sets, as large as possible, to get practical experience as preparation for the position. Seek education and constantly learn something new in order to stay relevant and demonstrate your excitement in this position. If you want to go above and beyond, keep a blog about what you’re learning and share it in the “Featured” section on your LinkedIn profile.

Become a Data Engineer

The Day-to-Day as a Data Engineer

What Skills Should Data Engineers Have?

Learn the Skills You Need to Become a Data Engineer

Data Science

Python

SQL

Machine Learning

R Programming

Mathematics

Data Engineer Salaries

Typical Qualifications to Become a Data Engineer

Searching for Data Engineer Jobs

Tips for Data Engineers

What Job Titles Would a Data Engineer Hold?

The Day-to-Day as a Data Engineer

What Skills Should Data Engineers Have?

Learn the Skills You Need to Become a Data Engineer

Data Science

Python

SQL

Machine Learning

R Programming

Mathematics

Data Engineer Salaries

Typical Qualifications to Become a Data Engineer

Searching for Data Engineer Jobs

Tips for Data Engineers

What Job Titles Would a Data Engineer Hold?

Related Careers

Salary Comparison to Data Engineer

Data Scientist

Machine Learning Engineer

Data Analyst