Salary for Senior-level / Expert Data Engineer in United States during 2024

Salary details

The average senior-level / expert Data Engineer salary lies between USD 123,900 and USD 199,600 in the United States. It represents the overall compensation/gross salary amount for the working year (before deductions like social security, taxes and other contributions), not including equity/stock options or similar benefits.

Region represents the primary country of residence of an employee during the year (or residence for tax purposes). All data shown are full-time equivalent (FTE) salaries. Part-time salary information has been extrapolated to its FTE value.

Last updated: Feb. 16, 2025

Salary trend

Top 20 Job Tags for Senior-level / Expert Data Engineer roles

The three most common job tag items assiciated with senior-level / expert Data Engineer job listings are Engineering, Python and SQL. Below you find a list of the 20 most occuring job tags in 2024 and the number of open jobs that where associated with them during that period:

Engineering | 8809 jobs Python | 8409 jobs SQL | 8243 jobs Pipelines | 7457 jobs AWS | 5827 jobs Data pipelines | 5736 jobs ETL | 5647 jobs Architecture | 5497 jobs Spark | 5259 jobs Computer Science | 4829 jobs Big Data | 4331 jobs Azure | 4301 jobs Agile | 3981 jobs Security | 3816 jobs Machine Learning | 3581 jobs Java | 3334 jobs Data quality | 3331 jobs GCP | 3155 jobs Airflow | 3153 jobs Snowflake | 3029 jobs

Top 20 Job Perks/Benefits for Senior-level / Expert Data Engineer roles

The three most common job benefits and perks assiciated with senior-level / expert Data Engineer job listings are Career development, Health care and Flex hours. Below you find a list of the 20 most occuring job perks or benefits in 2024 and the number of open jobs that where offering them during that period:

Career development | 6855 jobs Health care | 3681 jobs Flex hours | 2968 jobs Team events | 2175 jobs Startup environment | 2101 jobs Competitive pay | 2040 jobs Equity / stock options | 1910 jobs Flex vacation | 1700 jobs Salary bonus | 1698 jobs Insurance | 1546 jobs Parental leave | 1469 jobs Medical leave | 1272 jobs Wellness | 964 jobs 401(k) matching | 841 jobs Fitness / gym | 421 jobs Gear | 374 jobs Home office stipend | 350 jobs Transparency | 346 jobs Conferences | 294 jobs Relocation support | 292 jobs

Salary Composition for Senior Data Engineers

In the United States, the salary for a Senior Data Engineer typically comprises a base salary, bonuses, and additional remuneration such as stock options or other benefits. The base salary is often the largest component, making up about 70-80% of the total compensation package. Bonuses, which can be performance-based or company-wide, usually account for 10-20%. Additional remuneration, such as stock options, profit sharing, or other incentives, can make up the remaining 5-10%.

The composition can vary significantly depending on the region, industry, and company size. For instance, tech hubs like San Francisco or New York may offer higher base salaries and more substantial stock options due to the competitive market. In contrast, companies in smaller cities or less competitive industries might offer a higher percentage of bonuses to attract talent. Larger companies often provide more comprehensive benefits packages, including retirement plans and health insurance, which can add significant value to the overall compensation.

Steps to Increase Salary Further

To increase your salary beyond the median of $160,000, consider the following strategies:

• Specialize in Niche Areas: Developing expertise in emerging technologies or niche areas within data engineering, such as real-time data processing or machine learning infrastructure, can make you more valuable.
• Leadership Roles: Transitioning into leadership or managerial roles can significantly boost your salary. This might involve leading a team of data engineers or overseeing larger projects.
• Continuous Learning: Stay updated with the latest tools and technologies in data engineering. This could involve taking advanced courses or obtaining certifications in new technologies.
• Networking: Building a strong professional network can open up opportunities for higher-paying positions. Attend industry conferences, join professional groups, and engage with peers on platforms like LinkedIn.
• Negotiation Skills: Improve your negotiation skills to better advocate for higher pay during job offers or performance reviews.

Educational Requirements

Most Senior Data Engineer positions require at least a bachelor's degree in computer science, information technology, engineering, or a related field. However, a master's degree or even a Ph.D. can be advantageous, especially for roles that require a deep understanding of complex data systems or advanced analytics. Some employers may also consider candidates with equivalent experience in lieu of formal education, particularly if they have a strong portfolio of relevant work.

Helpful Certifications

While not always mandatory, certain certifications can enhance your credentials and demonstrate your expertise to potential employers. Some valuable certifications include:

• Google Professional Data Engineer: Validates your ability to design, build, and operationalize data processing systems.
• AWS Certified Big Data – Specialty: Demonstrates your skills in using AWS services to design and implement big data solutions.
• Microsoft Certified: Azure Data Engineer Associate: Focuses on designing and implementing data management, monitoring, security, and privacy using the full stack of Azure data services.
• Cloudera Certified Professional (CCP) Data Engineer: Recognizes your ability to perform core competencies required to ingest, transform, store, and analyze data in Cloudera's CDH environment.

Experience Requirements

Typically, a Senior Data Engineer role requires 5-10 years of experience in data engineering or related fields. This experience should include a strong background in data architecture, ETL processes, data warehousing, and proficiency in programming languages such as Python, Java, or Scala. Experience with cloud platforms like AWS, Azure, or Google Cloud is also highly desirable. Additionally, familiarity with big data technologies such as Hadoop, Spark, and Kafka is often required.

Related salaries

Want to contribute?