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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.
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 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.
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.
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.
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.
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 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.
A Data Engineer in the United States makes, on average, $131,751 annually, according to Indeed.com.
Salaries for Data Engineers vary by region within the the United States. Listed below are some Data Engineer salaries for specific areas with the United States compared with the average national salary:
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.
The job of the Data Engineer is ever-evolving and highly in-demand. They 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.
Data Engineers can look for jobs on these sites:
Data Engineers can find remote opportunities on these sites:
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.
Data Engineers can specialize in pipelines, databases, or work more broadly as generalists. At smaller companies, there will only be one generalist, while others have an individual Data Engineer for each specialization.
Data Engineer can look for these job titles:
A Data Scientist could upskill and become a Data Engineer or Machine Learning Engineer. Machine Learning Engineer is one of the most lucrative and technically demanding data positions. Machine Learning Engineers create computer programs that enable machines to take actions without being specifically directed to perform those tasks. Data Engineers create the infrastructure for data and format data into a useful system, which Data Scientists use to analyze large amounts of data.
If you’re not yet a Data Scientist, you can try to land a job as a Data Analyst. Data Analyst is the entry-level role in data. It is the lowest pay grade for this career path, but it is a fantastic place to start. A Data Analyst is responsible for collecting, processing, and analyzing data. They usually translate these numbers into actionable insights that help their employer make better business decisions.
Each of these roles requires some education in math, computer science, or both. These roles usually require a four-year degree, but some will accept a coding bootcamp certificate as a qualification. There are no standard certifications in this career path, but most vendors do offer certifications for databases. These certifications can help boost your application, but a strong resume is more important.
Data scientists collect, organize, and analyze large sets of data, providing analysis that is key to decision making. Governments, non-profits, and businesses of all types rely on data for forecasting, risk management, and resource allocation. Data scientists discover and analyze trends in data, and report their findings to stakeholders. They will use algorithms and models to simplify and mine data sets to create data-driven recommendations. Data scientists are needed across a handful of industries, especially the ubiquity of data and the reliance on it for business decision-making.Learn about becoming a Data Scientist
Machine Learning Engineers create computer programs that enable machines to take actions without being specifically directed to perform those tasks. This job combines computer programming and data science to enable systems to learn and improve from experience automatically by using machine learning, a subset of artificial intelligence.Learn about becoming a Machine Learning Engineer
Data analysts review large amounts of data to summarize, analyze, and visualize it and provide insights. Working from data from multiple, relevant sources, they create and maintain databases, and use statistical techniques to analyze the collected data. Data analysts must be able to communicate with others about what the data shows and to be able to provide realistic recommendations based on their analysis. Many industries such as healthcare, advertising, and retail rely on the work of data analysts to inform their business decisions and strategy.Learn about becoming a Data Analyst