If you enjoy working with computers, love to solve complex problems, and are a technical wizard with strong analytical skills. A career as a data engineer might be right for you.
Data engineers are key members of an enterprise data analytics team and are responsible for managing, optimizing, overseeing, and monitoring data retrieval, storage, and distribution throughout the organization. Data Engineers need to have a high level of technical skills, including a deep understanding of SQL database design and a variety of programming languages such as Java and Python.
Data engineers support software developers, database architects, data analysts, and data scientists, ensuring optimal and consistent data delivery architecture is applied to all ongoing projects. Data engineers are self-motivated and able to comfortably support the data needs of multiple teams, systems, and products.
Sample job description
Do you love building and pioneering in the technology space? Do you enjoy solving complex business problems in a fast-paced, collaborative, inclusive, and iterative delivery environment? At [Your Company Name], you’ll be part of a big group of makers, breakers, doers and disruptors, who solve real problems and meet real customer needs. We are seeking a data engineer who is passionate about marrying data with emerging technologies. As an ideal candidate, you have proven experience building data pipelines, transforming raw data into useful data systems, and optimizing data delivery architecture.
Typical duties and responsibilities
- Create, maintain, and test architectures
- Build large, complex data sets to meet functional/non-functional business requirements
- Identify, design, and implement internal processes to improve efficiency and quality
- Automate manual processes by using data
- Optimize data delivery
- Build analytic tools that provide actionable insights into performance metrics
- Work with executive, product, data, and design stakeholders to resolve data-related technical issues and support their data infrastructure needs
- Work with data and analytics experts to improve data system functionality
- Use programming language and tools
- Prepare data for predictive and prescriptive modeling
Education and experience
- Bachelor’s degree in computer science, information technology, or applied math
- Master’s degree a plus
- 5+ years of related experience
Required skills and qualifications
- Advanced knowledge of database systems like SQL and NoSQL
- Experience building and optimizing data pipelines, architectures, and data sets
- Experience performing root cause analysis on internal and external data and processes
- Exceptional analytical skills
- Experience manipulating, processing, and extracting value from large disconnected datasets
- Understanding distributed systems
- Knowledge of algorithms and data structures
- Good project management and organizational skills
Preferred qualifications
- Experience working in a fast-paced care facility
- Experience with data pipeline and workflow management tools
- Experience with AWS cloud services
- Experience with stream-processing systems
- Experience with Python, Java, C++, Scala, etc.
- Good communication collaboration, and presentation skills
Average salary and compensation
The average salary for a data engineer is $112,300 per year in the United States. Salary ranges can vary depending on education, certifications, additional skills, the number of years of experience.
| Location | Salary Low | Salary High |
|---|---|---|
| Phoenix, Arizona | $118,250 | $144,500 |
| Los Angeles, California | $133,400 | $163,050 |
| Denver, Colorado | $111,150 | $135,850 |
| Washington, DC | $135,400 | $165,500 |
| Miami, Florida | $110,650 | $135,250 |
| Orlando, Florida | $102,050 | $124,750 |
| Tampa, Florida | $103,050 | $126,000 |
| Atlanta, Georgia | $108,150 | $132,150 |
| Chicago, Illinois | $124,300 | $151,900 |
| Boston, Massachusetts | $134,400 | $164,250 |
| Minneapolis-St. Paul, Minnesota | $107,100 | $130,900 |
| New York City, New York | $141,450 | $172,900 |
| Philadelphia, Pennsylvania | $115,200 | $140,800 |
| Dallas, Texas | $112,150 | $137,100 |
| Houston, Texas | $111,650 | $136,500 |
| Seattle, Washington | $129,300 | $158,100 |
| National Average | $101,050 | $123,500 |
Typical work environment
Data engineers typically work as part of a team. Data engineers usually sit at a desk in front of a computer for long periods. Many work remotely, as they can connect to their servers from virtually anywhere. They work with the team to efficiently collect, extract, and process large amounts of data. The job can be stressful at times with the pressure of meeting deadlines and having to use a variety of tools and techniques.
Typical hours
The typical work hours for a data engineer in an office setting are 9 AM to 5 PM, Monday through Friday, but they might have to put in long hours behind the desk to complete projects and meet deadlines.
Available certifications
Data engineers work in a variety of industries, and many institutions offer certifications for IT professionals. Here are some specifically designed for data engineers:
- IBM Data Engineering Professional Certificate. The certificate is for entry-level candidates looking to stand out from their peers and develop job-ready data engineering skills. The self-paced online courses give you the essential skills you need to work with a variety of tools and databases to design, deploy, and manage structured and unstructured data. The course uses Python programming language and Linux/UNIX shell scripts where you’ll extract, transform and load (ETL) data. You’ll gain a working knowledge of relational databases (RDBMS) and query data using SQL statements, among other things. With numerous labs & projects, you’ll get hands-on experience utilizing the concepts and skills you learn. There are no eligibility requirements for this credential.
- Cloudera Certified Data Engineer (CCP). If you are an experienced open-source developer, earning the Cloudera Certified Data Engineer credential will demonstrate your ability to perform core competencies required to absorb, transform, store, and analyze data in Cloudera’s CDH environment. Candidates interested in the CCP Data Engineer credential should have in-depth experience developing data engineering solutions. The program includes transferring data, storing data, data analysis, and workflow.
- Google Cloud Certified Professional Data Engineer. The Google Cloud Certified Professional Data engineer credential ensures that you can design, build, secure, and monitor data processing systems, emphasizing compliance, scalability, efficiency, reliability, and portability. The exam assesses your skills in designing data processing systems, using machine learning models, ensuring solution quality, and using data processing systems. There are no prerequisites or requirements for this credential, however, it is recommended that you have 3+ years of industry experience, including 1+ years designing and managing solutions using Google Cloud.
Career path
The path to becoming a data engineer starts by earning a bachelor’s degree in computer science, information technology, applied math, or a similar field. Many data engineers begin in entry-level roles such as business intelligence analysts or database administrators. As they gain experience and pick up new skills, including cloud computing, coding skills, and database design, they can move on to more advanced roles as data engineers. Earning a master’s degree can open up higher-paying opportunities. Obtaining a certification can validate your skills and help you gain an edge with potential employers. With years of experience, data engineers can advance into managerial roles or become data architects, solutions architects, or machine learning engineers.
US, Bureau of Labor Statistics’ job outlook
SOC Code: 15-1253
| 2020 Employment | 1,847,900 |
| Projected Employment in 2030 | 2,257,400 |
| Projected 2020-2030 Percentage Shift | 22% increase |
| Projected 2020-2030 Numeric Shift | 409,500 increase |
Position trends
As more data products are serving external customers as opposed to internal ones, product-focused data engineering is becoming more favorable as a data engineering skill. Instead of catering to a specific industry, data engineers with the skills to build customizable and scalable data products that serve a variety of different customers will be in demand.
The roles of data scientists and data engineers will continue to blur as more organizations are hiring data engineers to handle their data from end to end, which includes statistics and modeling. Similarly, the trend of smaller companies combining the roles of data engineering, machine learning engineering, and DevOps into one position could take hold in the future, although finding someone with the skills to handle all three roles might be difficult.
With the acceleration of traditional on-premises databases transitioning or being deployed to the cloud, companies will see benefits in cost, time, reliability, and mobility. Data engineers will need good working knowledge of cloud computing.
Sample interview questions
- Which ETL Tools are you familiar with?
- What skills are important for a data engineer?
- What data engineering platforms and software are you familiar with?
- Which computer languages do you have experience using?
- How do you create reliable data pipelines?
- What is the difference between structured and unstructured data?
- How would you deploy a big data solution?
- Have you engineered a distributed system? How did you engineer it?
- Have you used data modeling?
- Which frameworks and applications are essential for a data engineer?
- Are you more database or pipeline-centric?
- How would you validate a data migration from one database to another?
- What are the pros and cons of cloud computing?
- How would you prepare to develop a new product?
- Which Python libraries would you use for efficient data processing?
- How would you deal with duplicate data points in an SQL query?
- How would you plan to add more capacity to the data processing architecture to accommodate an expected increase in data volume?
- What is the difference between relational vs. non-relational databases?
- Can you explain the components of a Hadoop application?
