Data engineers build massive reservoirs for data and are key in managing those reservoirs as well as the data churned out by our digital activities. They develop, construct, test, and maintain data-storing architecture — like databases and large-scale data processing systems. Much like constructing a physical building, a big data engineer installs continuous pipelines that run to and from huge pools of filtered information from which data scientists can pull relevant data sets for their analyses.
Data engineers typically have an undergraduate degree in math, science, or a business-related field. The expertise gained from this kind of degree allows them to use programming languages to mine and query data, and in some cases use big data SQL engines. Depending on their job or industry, most data engineers get their first entry-level job after earning their bachelor’s degrees. Here are five steps to keep in mind if you are planning on becoming a data engineer:
- Earn a bachelor’s degree and begin working on projects.
- Fine tune your analysis, computer engineering and big data skills
- Get your first entry-level job.
- Consider pursuing additional professional engineering or big data certifications.
- Pursue higher education degrees in computer science, engineering, applied mathematics, physics, or in a related field.
Given the importance of data engineering and big data across sectors, individuals with computer and information technology skills are in high demand. With more experience, degrees, and certifications, data engineers can rise to be leaders in the field.
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What is Data Engineering and Who Is a Data Engineer?
Data engineering is a highly variable, big-tent field with a primary focus on developing reliable mechanisms or infrastructure for data collection.
Who is a data engineer? A data engineer essentially is anyone who serves as a gatekeeper and facilitator for the movement and storage of data. Data engineers are also often tasked with transforming big data into a useful form for analysis. In order to do this, they design, construct, install, test, and maintain highly scalable data management systems — basically, software needed to store and use this data.
Steps to Become a Data Engineer
Professional opportunities within data engineering are many. From major Silicon Valley tech companies to small startups to healthcare systems, the data engineer helps businesses scale and make the most of their data resources.
But you have to be aware of what it takes to get your foot in the door as a data engineer and stay relevant in the field.
Data engineer veteran, David Bianco, has made a successful career of building geospatial data pipelines, starting with Esri’s ArcGIS Online and now is at Urthecast, based out of San Francisco, CA. Bianco urges prospective data engineers to become fluent in programming languages and tools that can help them get hired, and understand what those tools can help them accomplish.
“Languages come and go, so it’s better to gain a full understanding of the concepts behind building a robust pipeline,” he says.
Consider Bianco’s advice and these key steps if you want to build a career as a data engineer:
1. Earn a bachelor’s degree and begin working on projects
Anyone who enters this field will need a bachelor’s degree in computer science, software or computer engineering, applied math, physics, statistics, or a related field. You’ll also need real-world experience, like internships, to even qualify for most entry-level positions. For those who choose college majors outside of these fields, ensure you take courses on data structures, algorithms, database management, or coding. It’s important that you learn as much as you can.
Join a study group, attend a hackathon with friends, or take on personal projects with classmates and build a portfolio you can eventually show potential employers.
2. Fine tune your analysis, computer engineering and big data skills
You’ll need to hone your expertise in SQL, one of the foundational programming languages data engineers speak. This is necessary because most data is stored in relational database systems. Engineers use SQL to query data and SQL engines, such as Apache Hive, to then analyze this data.
Data engineers should also have an understanding of other programming languages that help with statistical analysis and modeling, such as Python or R. A mastery of Spark, Hadoop, and Kafka will come in handy, too.
Beyond a mastery of language, other key skills include using database architectures, understanding machine learning, finding data warehousing solutions, knowing how to construct data pipelines, data mining, and utilizing cloud platforms like Amazon Web Services.
Data management technology is constantly evolving so it is also important for data engineers to have their hand on the pulse of what’s happening in their field.
3. Get your first entry-level engineering job
Your first job may or may not involve engineering, but even if it is IT-related, you’ll gain invaluable insights on how to approach data organization challenges. That first job will challenge you to think creatively and find unusual ways to solve problems. Why is this important? You’ll quickly learn that data engineers don’t do it all by themselves. Instead, they listen to management, data scientists and data architects — it’s a completely collaborative field. During this experience, you may also gain an understanding of the way your chosen industry functions in the real world and how data can be collected, analyzed and utilized.
4. Consider pursuing additional professional engineering or big data certifications
To advance a career in data engineering, it is often necessary to pursue certifications. If you hope to boost specific skills, you’ll find a lot of vendor-specific certifications such as Oracle, Microsoft, IBM, and Cloudera, among others. With so many options available, be sure to speak with mentors to determine which certification is worth your time and money and study the descriptions of jobs you have your eye on to see what certifications might be required. The most general certification you can obtain is the Certified Data Management Professional or CDMP. Developed by the Data Management Association International (DAMA), the CDMP is a solid, all-round credential for general database professionals. Many employers will recognize the acronym on your résumé.
5. Pursue higher education degrees in computer science, engineering, applied mathematics, physics, or a related field
Many engineers succeed without higher education, but you may also want to consider a master’s degree in computer engineering or computer science degree to fine tune your skills, expand your knowledge, or start working as a data engineer or a data scientist.
Not all jobs require a master’s in data engineering. Some employers are willing to accept relevant work experience and proof of technical expertise in lieu of a higher degree.
Data Engineer Responsibilities
Data engineering is a highly strategic job with many responsibilities spanning from construction of high-performance algorithms, predictive models, and proof of concepts, to developing data set processes needed for data modeling and mining.
Here is an overview of data engineer responsibilities:
- Ensuring that data storage and collection systems meet business requirements and acceptable industry standards.
- Integrating new data management software into a company’s existing structures or research new opportunities for a business’ data acquisition. This could mean helping a company come up with a new way to efficiently bring in data from a brand-new client.
- Creating custom software components using a wide range of languages and tools — like scripting languages — to merge different systems together or develop a strong analytics infrastructure for measuring your data stored by a business.
- Storing and processing data securely at all times. Data engineers remain on the frontlines of a company’s cyber defenses, installing and updating disaster recovery protocols, in addition to recommending ways to improve data reliability and quality.
Becoming a data engineer can be an opportunity to collaborate with an interdisciplinary group of people, working closely with data architects, modelers, and IT specialists to achieve different project goals.
Data Engineer vs Data Scientist
While a data engineer is responsible for building, testing, and maintaining big data architectures, the data scientist is responsible for organizing big data within the architecture and performing in-depth analyses of the data to help develop insights and solve business needs.
These two professionals often work closely together. A data scientist can’t interpret anything unless there is a data engineer to build the tools for storing and processing that data.
Data Engineer Jobs
The data engineering field is one that is constantly evolving. Shifts in the industry like the evolution of Hadoop, which is increasingly being used as an enterprise data hub, advances in processing power for predictive analytics, and a general move toward the Cloud could make a data engineer’s life more complicated. But it also presents more job opportunities.
You can work as a data engineer, a senior cloud data engineer, a senior data engineer, and a big data engineer, among other roles.
Basically, it’s an exciting time to be a data “builder.” If you love playing with new tools and can think outside the relational database box, you’ll be in a prime position to help companies adapt to the demands of this industry.
Data Engineer Salary for 2020: How Much Does a Data Engineer Make?
The Bureau of Labor Statistics reports that employment opportunities in the computer and information technology field as a whole is projected to increase by 12 percent in the between 2018 and 2028. That means about 546,200 new jobs will be added throughout the industry. Here’s an overview of average annual wages for some positions available:
Database Administrator – In May 2018, the median annual salary for database administrators was $92,030, according to the Bureau of Labor statistics.
Data Engineer – Data engineers earn an average annual salary of $108,808, according to a 2017 IBM report [PDF, 3.9 MB].
Data Scientist – Data scientists earn an average annual salary of $105,676, according to a 2017 IBM report [PDF, 3.9 MB].
Note: Salary information was retrieved from the Bureau of Labor Statistics and “The Quant Crunch,” a 2017 report from IBM.
Summarizing Data Engineer Checklist
Here’s a quick refresher of what you need to know about the path to becoming a data engineer:
- Earn a bachelor’s degree – Earning a bachelor’s degree in computer science or programming, or a related field is a good way to familiarize yourself with the field of data engineering. Through your studies, you can even identify a specialty area that you would eventually want to work in.
- Hone those big data skills – Employers in the field are looking for candidates with unique skills and a strong command of software and programming language. Develop your skillset through practice, personal projects and continuing education.
- Obtain an entry-level job, even if in IT — gain experience. You can continue to build on that experience and learn about big data trends and solutions that you weren’t aware of before.
- Get certifications to further specialize and help to make you a competitive candidate as you apply for new roles and advance in your career.
- Pursue a master’s in data engineering. A graduate degree is one way to stand out as a competitive candidate in the job market. It demonstrates that you’ve taken additional steps to further your knowledge.
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