Data Engineer

A career as a data engineer entails building and planning systems that offer data scientists accessible and interpretative data. The duties of a data engineer are integral to enabling data analytics.

What Is Data Engineering?

Data engineering is a career focused on data collection and creating the infrastructure that allows for data analysis. A data engineer assembles, validates, and maintains accumulated data sets, enabling data scientists to use the data for analysis to answer questions and identify patterns.

Data engineering is a highly skilled job in providing consistent, organized data flow to enable data-driven work. A data engineer's work can include:

  • System architecture
  • Data storage and processing
  • Data modeling

Data Engineering vs. Data Science: What’s the Difference?

While both careers focus on data, data engineering and data science differ in many ways. A data engineer builds systems that give data scientists the ability to access the data. A data engineer maintains that data infrastructure while a data scientist interprets the data to do things like build and train predictive models.

Data Engineer Job Description

Typical Responsibilities and Tasks

Common job duties of a data engineer are to:

  • Create, build, test, and maintain data architectures
  • Use business requirements to align data to system architecture
  • Create data set processes to automate data delivery
  • Extract and load data into databases
  • Prepare data for prescriptive and predictive modeling
  • Improve data quality, efficiency, and reliability

Skills and Traits Used

In a data engineer career, there are many skills essential to the job. To build data pipelines and warehousing solutions, a data engineer should have the following technical skills.

  • Build database APIs so analysts can query the data
  • Use Extract, Transfer, Load (ETL) tools to pull, clean, and transfer data into a database
  • Manipulate database systems for data storage and retrieval
  • Understand AWS and other data warehousing solutions
  • Know Apache Hadoop or similar frameworks for creating distributed systems

Programming languages that may be used in data engineering are:

  • Python
  • SQL
  • Java
  • Scala
  • C++

Throughout your career as a data engineer, many additional soft skills are required to perform your job well. These traits show employers a robust skill level that exceeds your education and experience:

  • Self-Starter. Sometimes a business cannot tell you exactly what they need, and, instead, you are the person they turn to for answers. Being a self-starter is a crucial attribute.
  • Problem Solver. Being able to solve problems makes you an impactful and integral part of the team, including for strategic decisions that involve data or data insight.
  • Team Player. Data engineers work directly with others frequently. Teamwork and collaborative skills are integral to communication between the business and the data engineer.
  • Curiosity. Asking questions gives better answers. Data engineers may ask things like, “How can we make this better?” Or, “Who uses this data, and how will it be easier to use?”

Salary and Job Outlook for Data Engineering

Data engineering is a fast-growing career. The median annual wage for database administrators was $93,750 in May 2019, according to the U.S. Bureau of Labor Statistics (BLS).

Glassdoor shows a national salary of $102,864 for a data engineer in the United States. According to, the average salary range for a data engineer is between $90,163 and $125,713, with a median average of over $107,000.

An entry-level data engineer with less than a year’s experience can expect a total salary of over $77,000. For those with 1 to 4 years of experience but early in their career, that number grows to around $87,000. The numbers continue to rise depending on experience, with a 20-year career veteran making the highest at almost $114,000. As with any career, those who live in high cost-of-living areas and/or have a demonstrated track record of success can earn significantly more.

Where You Can Work As a Data Engineer

Many industries rely on the support of a data engineer, and the opportunities are vast. With the explosion of data availability, many companies across industries recognize the need to analyze it to understand their users or other trends. These include hospital and healthcare industries, IT, financial services, and computer software companies. Whether it’s a small start-up or a company in the Silicon Valley tech industries, businesses need data engineers to make the most of their data resources. Your job title might be data engineer, senior cloud data engineer, senior data engineer, or big data engineer, among other roles.

Some larger companies who rely on data engineers include:

  • Microsoft. With over 1,600 data employees, they are among the largest employers of those with a career in data engineering.
  • Facebook. Another company ranking high in need of data engineers, Facebook, has over 1,100 high-level data team members.
  • IBM. IBM is a leader in data engineering hiring practices, with over 1,200 team members, including 28 big data engineers and 166 data engineers.

Other big companies that need data engineers are Amazon, Google, Apple, and Oracle. Examples of pay scales for these larger companies are:

  • Amazon. Salary range is $78,000 - $133,000, with an average annual salary of $103,849.
  • Hewlett-Packard. Salary range is $64,000 - $105,000, with an average annual salary of $86,164.
  • Facebook. Salary range is $93,000 - $171,000, with an average annual salary of $122,695.
  • IBM. Salary range is $90,000 - $116,000, with an average annual salary of $99,351.

Typical Workplace Settings

Gluing scripts and writing code is an everyday activity for a data engineer. You do a majority of data engineering in Java, yet skills in ETL, R, Python, and SQL are integral.

There are many advantages to a data engineering career choice. The work atmosphere is typically an office atmosphere. Though you will need to coordinate with other data engineers (if your team is big enough) and meet with data analysts, data scientists, business managers, or clients, there will typically be significant "heads-down" time for you to do your work. Your job will consist of maintaining and advancing infrastructure -- so you'll need to gain job satisfaction from this backend work, where you may not be able to take the credit for higher-profile results from data analysis or data mining.

Educational Requirements for Data Engineering

The first step in a data engineer career is your education. Data engineers typically have an undergraduate degree in science, math, or a business-related field. A bachelor’s degree in software engineering, computer science, or information technology is an ideal choice to start. Within these programs, you’ll learn things like:

  • Software design
  • Information security
  • Data mining
  • Database management
  • Data structures

While many jobs will not require a master’s degree in the job post, a master's degree may give you a competitive edge and may be particularly helpful if you are switching from another background or received your bachelor's in a very different field. Some examples of well-regarded universities offering a master’s degree in data engineering include:

  • Purdue University: Krannert School of Management
  • DePaul University
  • The University of Rochester
  • New York University
  • Carnegie Mellon University
  • Columbia University
  • North Carolina State University
  • Georgia Institute of Technology
  • The University of Oklahoma
  • The University of Iowa

When looking for a data engineering program, ABET accreditation is not a must-have, but it certainly shows the program meets engineering education standards. Some employers may prefer graduates of ABET-accredited programs. Whether or not you should pursue an accredited program depends on the employer and your desire to showcase additional education credentials.

Data Engineering Boot Camps

For those who want to quickly pick up skills and credentials in the field of data engineering without a degree, there are also online boot camps. Bootcamps range in the curriculum offered. For example, the NYC Data Science Academy offers training in Spark, R, Python, Hadoop, GitHub, and SQL with a focus on real-world applications. Others offer skills that range from interactive coding challenges to popular data tools such as Pandas, Numbpy, Scipy, Matplotlib, and Bokeh.

Each varies in length and cost. The RMOTR Bootcamp lasts four months while Dataquest offers a 24-week schedule. The data science career track at Springboard offers a six-month program preparing you for an interview-ready portfolio. Choosing one depends on your needs, how much time you can devote, and the cost.

Certifications in Data Engineering

With data management technology constantly evolving, it is crucial to have insight into what’s happening in the field. Certifications are one way to stay on top of trends and showcase your knowledge more formally. Certifications can include vendor-specific certifications. Some are:

  • Cloudera. This certification covers requirements for developmental and administrative expertise in Apache Hadoop infrastructures built around the Cloudera platform.
  • IBM. This covers nine courses including SQL, Python, open-source tools, data science methodology, and more.
  • Microsoft. Microsoft offers many certifications. One of the most highly sought after is the Microsoft Certified: Azure Data Engineer Associate. The course path has three levels that include fundamentals, associate, and expert.
  • Oracle. Oracle allows you to browse certifications by product. Certifications range from Java and Middleware to Virtualization and Platform as a Service (PaaS).

You may also earn a Certified Data Management Professional (CDMP) recognition. Developed by the trade association DAMA International, the CDMP is a solid all-around credential for general database professionals. Many employers recognize this certification, and having it on your resume can often be an extra point in your favor.

A career in data engineering takes hard work and determination, but the payoff can be rewarding, both in terms of job impact and salary. You’ll learn more along your career path and have options to build on your education to stay in touch with the changing world of technology.

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