IN THIS ARTICLE
- What Does a Data Engineer Do?
- How Much Do Data Engineers Make?
- How To Boost Your Data Engineer Salary
- FAQs About Working as a Data Engineer
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The digital transformation of our global economy has yielded an unprecedented increase in data production. Massive volumes of data are generated at high velocity every day, and industries from finance to healthcare are now leveraging big data to optimize their operations.
The catch? Data cannot be used as-is to inform organizational decision-making or develop business solutions. Its value is only realized after it has been properly collected, processed, and stored—which is where data engineers come in.
What Does a Data Engineer Do?
Data engineers create and manage data ecosystems that capture, clean, and transform raw, unstructured data into formatted, structured data that can be analyzed and interpreted. Data engineers are responsible for constructing infrastructures that collect, process, and store data, and must also develop and deploy machine learning models that can scan, label, and categorize that data.
The work of data engineers crucially supports other data functions. Data scientists and data analysts rely on data pipeline architectures to deliver usable data that will yield insights related to key business performance metrics.
How Much Do Data Engineers Make?
Data engineer salaries vary according to location, industry, role, and level of experience. Read on to learn more about how much you could make as a data engineer.
Data Engineer Salaries by Role
The field of data engineering encompasses a variety of roles related to machine learning, big data management, and more. Some data engineers at small companies are generalists, while others at mid-sized and large organizations have pipeline or database-focused roles. Here’s how data engineer salaries vary by role.
According to Indeed, the average base salary for a data engineer is $116,996 plus a $5000 cash bonus. The primary duty of a data engineer is to support data stakeholders across an organization. Data engineers strive to ensure that access to high-quality data is readily available through designing and fabricating efficient, scalable data pipelines and leveraging a combination of data expertise and business intelligence to address infrastructural issues.
Source: Job listing, Meta, 2022
Big Data Engineer
According to Glassdoor, big data engineers earn an average yearly salary of $104,463. Big data engineers work with vast data processing systems in large-scale environments. These professionals focus on distributed, scalable data ecosystems and build pipelines that support massive amounts of data.
Source: Job listing, TikTok, 2022
Machine Learning Engineer
According to Indeed, the average annual salary for a machine learning engineer is $131,001.
Machine learning engineers use software engineering and data science techniques to design and deploy machine learning systems. Machine learning engineers develop ML applications, optimize ML solutions for scalability and performance, analyze data, create and test machine learning models, and perform foundational data engineering tasks to improve data flow within a data ecosystem.
A machine learning data engineer focuses on data ingestion, transformation, and presentation for machine learning solutions. This requires a thorough understanding of distributed data systems as well as machine learning platforms.
Source: Job listing, Apple, 2022
According to Glassdoor, the average annual salary for a data architect is $118,868.
Data architects translate an organization’s business needs into technology requirements in order to design a strategic data management framework. Data architects possess high-level knowledge of an organization’s data assets and products and oversee the development of data architecture and models. Finally, data architects are responsible for establishing standards of data representation, distribution, archiving, retention, and protection within an organization.
Data Engineer Salaries by Industry
Demand for data-driven solutions is on the rise across industries. Here’s how data engineering salaries in major sectors stack up.
Data engineering compensation in the tech industry varies by location, experience, and employer, but average base salaries at large companies generally range from $86,288 on the lower end to $171,980 on the higher end according to Glassdoor reports.
Tech companies like Uber, Doordash, and Amazon are leveraging real-time data analytics to automate operations, personalize user experience, and boost revenue growth. Fresh data is highly valuable, and as a result, many data engineers working in the tech industry focus on building real-time data streaming and data processing pipelines.
Issues of scale are another primary data engineering concern for tech companies. Data engineers in the tech sector must design systems that can scale to handle massive volumes of across a range of departments and data types.
Source: Job Listing, Amazon, 2022
According to Glassdoor, the average salary for a healthcare data engineer is $109,717.
Data is used in healthcare to improve patient outcomes, streamline insurance claim processes, optimize staffing and operations, plan resource allocation, and drive product development. The myriad applications of data in healthcare create a diverse range of data engineering job opportunities in the industry. A data engineer working in healthcare product development might build data systems that support AI-powered medical imaging analysis, while a data engineer working for a hospital system might design data architectures that manage and integrate electronic medical records.
Because healthcare is a regulated industry, data infrastructures must meet extensive compliance and auditability requirements. Healthcare is a high-stakes service, meaning data systems must consistently and efficiently deliver high-quality data to detailed models. These factors make complexity a primary data engineering concern within the healthcare industry.
Source: Job listing, Glassdoor, 2022
On average, data engineers working for the federal government command an average annual salary in the range of $117,032 – $126,963 according to Glassdoor. Municipal roles tend to pay less—a data engineer working for the City of Minneapolis, for example, can make between $77,045 – $106,036 depending on experience.
Federal agencies are now harnessing data to manage education, taxation, social services, transportation, and more. The Social Security Administration (SSA) uses data to analyze claims, while the Federal Housing Authority (FHA) uses data to forecast claim, default, and repayment rates. The Department of Defense is also slated to accelerate AI adoption in 2022.
Data is also being used to direct policy at state and local levels. New York City has used data analytics to identify and investigate instances illegal housing discrimination committed by landlords against tenants. Chicago has used data analytics to tackle and curb rodent infestations.
A data engineer working in municipal government will have ample opportunity to effect change, but technological innovation works differently in the public sector, explains Eliza Pollack of the City of Philadelphia’s Office of Innovation and Technology. Procurement rules, political relationships, and a slower pace of business create a work environment that contrasts with the “move fast and break things” approach that characterizes certain private-sector settings.
According to ZipRecruiter, data engineers in the financial services sector earn an average salary of $104,114.
Big data now plays an integral role in the financial industry, with applications ranging from fraud detection to risk management and automated trading. Furthermore, financial services from mobile banking to wealth management platforms create thousands of sensitive data points each day.
Data engineers in the financial sector must create systems and strategies to secure, manage, and make use of this data. All data management systems must prioritize privacy and meet regulatory compliance requirements—meaning that financial data engineers are responsible for building complex, high-stakes applications.
Source: Job listing, PayPal, 2022
Data Engineer Salaries by Experience
Data engineer salaries increase with experience. Let’s explore how much you can expect to earn at different stages in your data engineering career.
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Entry Level Data Engineer Salary
On average, entry-level data engineers with less than a year of experience in the field take home $77,614 annually—a total figure that includes bonuses and overtime compensation. With just a few more years of experience—one to four years, to be precise—an early career data engineer can take home an average total of $88,202.
Entry-level data engineers focus on debugging, testing, and executing smaller projects that help maintain data infrastructures. Under the guidance of more senior data engineers, entry-level data engineers are able to hone core competencies like troubleshooting, advanced coding, data design, and more.
Source: Job listing, John Deere, 2022
Mid Level Data Engineer Salary
A mid-level data engineer takes home an average of $103,872 annually. These professionals typically have five to nine years of experience in the field, although some may reach this career stage earlier.
Mid-level data engineers start to assume more project management responsibilities, particularly regarding interdepartmental collaboration. A mid-level data engineer may start working more closely with data scientists, data analysts, and project managers to create data-driven solutions. Many data engineers begin to explore and develop unique specializations at this career stage.
Source: Job listing, Altamira, 2022
Senior Data Engineer Salary
A senior data engineer earns an average total of $117,464 annually. Senior data engineers often have 10+ years of experience in the field and are responsible for overseeing and assigning tasks to more junior teams.
Senior data engineers focus on data strategy and must define data requirements, architect data infrastructures, and map out greater data initiatives. They collaborate extensively with data science teams to develop scalable, finely-tuned pipelines and intuitive data models and must communicate with external clients and internal stakeholders about data goals and how their data can be leveraged.
Equipped with a fluent, nuanced understanding of data, senior data engineers preside over complex problems and balance the prioritization of immediate and long-term solutions.
Source: Job listing, Netflix, 2022
Data Engineer Salaries by Education
Although some employers prioritize applied skills and hands-on experience over formal degrees, education can affect your starting salary as a data engineer. Here’s how data engineering salaries break down according to education:
A data engineer without a degree or work experience will likely start in an entry-level role, for which average total compensation hovers around $77k.
Data engineering is an emerging field that lies at the intersection of software engineering and data science. As a result, very few undergraduate and graduate programs offer specific degrees in data engineering—meaning that many data engineers cultivate their skills in shorter, more affordable data engineering bootcamp programs. Although some employers will look for candidates with a bachelor’s degree in computer science, others will accept equivalent training with data management tools and techniques. Many data engineering skills are built through practical experience rather than formal degree programs. Employers often prefer candidates to hold a bachelor’s degree, but a degree is not always a requirement.
Source: Job listing, Zippia, 2022
Data engineers who hold a bachelor’s degree earn a median annual income of $97,466. Typically, data engineers earn degrees in computer science, software engineering, statistics, math, or other related fields, and may need to acquire specific data engineering skills through self-learning or bootcamp programs. Many data engineering jobs require a bachelor’s degree of some kind.
Source: Job listing, Zippia, 2022
Data engineers who hold a master’s degree earn a median annual income of $106,629. Because data engineering master’s programs are rare, data engineers are more likely to hold master’s degrees in computer science, engineering, and other related areas of study.
Source: Job listing, Zippia, 2022
How To Boost Your Data Engineer Salary
Skills, certifications, and your ability to negotiate the terms of your employment can affect your salary. Let’s take a look at how to command higher compensation in a data engineering role.
Expand Your Skill Set
Choose the Right Location
Your location will affect your compensation as a data engineer. According to Indeed, these are the top-earning cities for data engineers by average base salary:
San Francisco, CA: $150,077
Plano, TX: $135,145
Austin, TX: $132,347
Los Angeles, CA: $131896
New York, NY: $129,401
Chicago, IL: $126,160
Boston, MA: $121,564
San Jose, CA: $117,793
Dallas, TX: $116,685
Washington, Oregon, California, Texas, and New York are among the top 10 states on Zippia’s list of the best states for data engineers.
Gain More Experience
A data engineering internship can help you build a vital hands-on experience that might boost your salary in an entry-level role. In your free time, contribute to open source data engineering projects, and document your work on GitHub or StackOverflow. Some team leaders will even comb those forums to source new talent.
Negotiate Your Job Offer
To successfully negotiate a job offer, research compensation packages at similar companies for comparable roles. Glean this information from peers, mentors, and salary comparison tools like LinkedIn, Glassdoor, and Blind. Look at salaries within your industry that match your level of experience and job title.
To up your final offer, emphasize and quantify the value you will add to the organization, and articulate how this should affect your salary. Remember that in addition to salary, benefits, title, hours, flexibility options, and more are negotiable as well. Use any competing job offers to negotiate a better contract.
Pursue a New Degree or Certification
A data engineering certification will verify proficiency with popular tools and can boost your bargaining power. In-demand data engineering credentials include the Google Cloud Professional Data Engineer certification, the Cloudera Data Platform (CDP) certification, the AWS Certified Big Data Specialty certification, the Azure Data Engineer Associate certification, the IBM Certified Solution Architect – Data Warehouse V1 certification, and the SAS Certified Big Data Professional certification.
FAQs About Working as a Data Engineer
Wondering if a career in data engineering is right for you? Read on for answers to frequently asked questions about working as a data engineer.
Is Data Engineering a Good Career?
Data engineering is a rewarding, challenging field with ample opportunity for career growth and professional advancement. Demand is on the rise for data-driven business strategies, creating an ever-expanding need for engineering professionals who can store, process, and analyze that data. Data engineering and related roles have exhibited 35% average annual growth according to LinkedIn’s 2021 Jobs on the Rise report.
Do Data Engineers Get Paid Well?
Data engineers rank in the top 10 highest-paid technology jobs. In fact, 51% of workers who switched jobs to pivot into data engineering in the second half of 2021 received a base salary increase of at least 20%.
How Much Money Can a Data Engineer Make?
With 10 years of experience, a data engineer can make 150K (indeed)—but top-tier companies may pay much more. At Netflix, for example, the average data engineer salary is 258,110, 105% higher than the national average. At eBay, the average data engineer salary is $182,279—45% above the national average. Average data engineer salaries at Meta clock in at $175,139—39% above the national average.
Do You Need a Degree To Be a Data Engineer?
Data engineering is a developing field, and as a result, data engineering degree programs are few and far between. A degree in data engineering is not necessary to break into the field, although many data engineers do hold degrees in computer science, statistics, or other related areas. Specialized data engineering skills can be built through self-learning, massive open online courses, or bootcamp programs.
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