Data Engineering Salaries 2023

The salary ranges provided in blog post are based on general research and analysis of various sources, such as Glassdoor, Payscale, LinkedIn, MadeIn, Levels.fyi and industry reports. For the most accurate and up-to-date salary information, we recommend conducting your research using multiple sources and considering your specific situation, skills, and the job market in your area.

Data engineering is a rapidly growing field, with increasing demand for skilled professionals who can design, build, and manage data pipelines and infrastructure. In this comprehensive guide, we will explore data engineering salaries, considering factors such as experience, job role, location, and industry. We will also discuss ways to boost your earning potential and tips for negotiating a competitive salary.

Several factors influence data engineering salaries, including:

• Experience: As with most professions, experience plays a significant role in determining salary. More experienced data engineers typically command higher salaries, reflecting their advanced skills and expertise.
• Job role: Data engineering encompasses a range of job roles, from entry-level positions to senior and managerial roles. Salaries vary depending on the level of responsibility and the complexity of tasks.
• Location: Salaries for data engineers can vary widely depending on the location, with higher salaries often found in major technology hubs or cities with a high cost of living.
• Industry: The industry in which a data engineer works can also influence salary, with certain sectors, such as finance or technology, typically offering higher compensation.

Data Engineering Salaries by Experience

Data engineering salaries tend to increase with experience, reflecting the growth in skills, expertise, and responsibility that comes with time spent in the field. The following ranges are approximate and can vary depending on the factors mentioned above.

1. Entry-level (0-2 years of experience): Entry-level data engineers, sometimes referred to as junior data engineers or data engineering analysts, typically earn between $60,000 and $125,000 per year.
2. Mid-level (3-5 years of experience): Mid-level data engineers, often holding titles such as data engineer or senior data engineer, can expect to earn salaries in the range of $80,000 to $130,000 per year.
3. Senior level (6+ years of experience): Senior data engineers, who may hold titles such as lead data engineer or data engineering manager, can command salaries between $100,000 and $190,000 per year or higher.

Data Engineering Salaries by Job Role

Different job roles within data engineering come with varying levels of responsibility and complexity, which can impact salary levels.

• Data Engineer: A data engineer is responsible for designing, building, and maintaining data pipelines and infrastructure. Salaries for data engineers can range from $60,000 to $190,000 per year, depending on experience and other factors.
• Data Engineering Manager: Data engineering managers oversee teams of data engineers, guiding the design and implementation of data pipelines and infrastructure. Salaries for data engineering managers can range from $125,000 to $220,000 per year, with variation depending on the size of the team and the scope of responsibility.
• Big Data Engineer: Big data engineers specialize in working with large-scale, distributed data processing systems, such as Hadoop or Spark. Salaries for big data engineers can range from $105,000 to $220,000 per year, depending on experience and other factors.
• Machine Learning Engineer: Machine learning engineers often work closely with data engineers, focusing on the design, implementation, and optimization of machine learning models and pipelines. Salaries for machine learning engineers can range from $90,000 to $250,000 per year, depending on experience and other factors.

Data Engineering Salaries by Location

Salaries for data engineers can vary widely depending on the location, with higher salaries often found in major technology hubs or cities with a high cost of living. The following are examples of average data engineering salaries in several major U.S. cities:

• San Francisco, CA: $125,000 - $250,000 per year
• New York, NY: $115,000 - $190,000 per year
• Seattle, WA: $100,000 - $170,000 per year
• Austin, TX: $85,000 - $170,000 per year
• Chicago, IL: $80,000 - $160,000 per year
• Atlanta, GA: $75,000 - $150,000 per year

It is essential to consider the cost of living when comparing salaries across different locations. A higher salary in a city with a high cost of living may not necessarily result in a better quality of life compared to a lower salary in a more affordable location.

Data Engineering Salaries by Industry

The industry in which a data engineer works can also influence salary levels. Some industries, such as finance or technology, typically offer higher compensation. The following are approximate salary ranges for data engineers in various industries:

• Technology: $85,000 - $250,000 per year
• Finance: $90,000 - $200,000 per year
• Healthcare: $75,000 - $190,000 per year
• Retail: $70,000 - $140,000 per year
• Manufacturing: $70,000 - $130,000 per year

Boosting Your Earning Potential

There are several ways to increase your earning potential as a data engineer:

• Develop specialized skills: Specializing in a specific area of data engineering, such as big data or machine learning, can help you command a higher salary.
• Gain certifications: Obtaining certifications, such as those offered by AWS, Google Cloud, or Microsoft Azure, can demonstrate your expertise and commitment to the field, potentially leading to higher salaries.
• Network and build relationships: Networking and building relationships with others in the industry can help you uncover new job opportunities and negotiate higher salaries.
• Continue your education: Pursuing a master's degree or other advanced education in data engineering, computer science, or a related field can enhance your skills and increase your value to employers.

Negotiating a Competitive Salary

When negotiating a salary for a data engineering position, keep the following tips in mind:

• Research salary data: Use resources such as Glassdoor, Payscale, or industry reports to research average salaries for data engineers in your area, taking into account factors such as experience, job role, and industry.
• Focus on your value: Highlight your skills, experience, and accomplishments to demonstrate your value to the employer and justify your desired salary.
• Be prepared to compromise: Enter negotiations with a salary range in mind, rather than a specific figure, and be prepared to compromise within that range to reach an agreement.
• Consider non-monetary benefits: Be open to negotiating non-monetary benefits, such as flexible work arrangements, additional vacation days, or professional development opportunities, which can enhance your overall compensation package.

In summary, data engineering salaries can vary widely depending on factors such as experience, job role, location, and industry. By understanding the factors that influence salary levels and taking steps to boost your earning potential, you can position yourself for a rewarding and lucrative career in data engineering. Additionally, being well-prepared for salary negotiations and considering non-monetary benefits can help you secure a competitive compensation package that aligns with your skills, experience, and value to the organization.