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

Salary details

The average senior-level / expert DevOps Engineer salary lies between USD 113,500 and USD 185,000 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: Nov. 10, 2024

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

The three most common job tag items assiciated with senior-level / expert DevOps Engineer job listings are DevOps, Python and Engineering. 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:

DevOps | 316 jobs Python | 292 jobs Engineering | 241 jobs Kubernetes | 237 jobs AWS | 233 jobs CI/CD | 229 jobs Security | 209 jobs Terraform | 192 jobs Pipelines | 190 jobs Machine Learning | 178 jobs Docker | 172 jobs Azure | 150 jobs Computer Science | 146 jobs Jenkins | 139 jobs Linux | 135 jobs Architecture | 128 jobs Ansible | 123 jobs GCP | 119 jobs Agile | 103 jobs Git | 95 jobs

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

The three most common job benefits and perks assiciated with senior-level / expert DevOps Engineer job listings are Career development, Flex hours and Health care. 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 | 221 jobs Flex hours | 117 jobs Health care | 103 jobs Startup environment | 86 jobs Competitive pay | 70 jobs Team events | 69 jobs Equity / stock options | 65 jobs Flex vacation | 59 jobs Insurance | 45 jobs Medical leave | 37 jobs Salary bonus | 34 jobs Parental leave | 30 jobs 401(k) matching | 23 jobs Wellness | 19 jobs Gear | 18 jobs Home office stipend | 16 jobs Flexible spending account | 15 jobs Transparency | 10 jobs Conferences | 10 jobs Relocation support | 8 jobs

Salary Composition

In the United States, the salary composition for a Senior-level/Expert DevOps Engineer in AI/ML/Data Science typically includes a combination of a fixed base salary, performance bonuses, and additional remuneration such as stock options or equity, especially in tech companies. The base salary often constitutes the majority of the total compensation package, ranging from 70% to 85%. Performance bonuses can vary significantly, often comprising 10% to 20% of the total compensation, depending on the company's performance and individual contributions. Additional remuneration, such as stock options, can be a significant part of the package, particularly in startups or large tech firms, and may account for 5% to 15% of the total compensation.

Regional differences also play a role; for instance, salaries in tech hubs like San Francisco, Seattle, or New York City tend to be higher due to the cost of living and competitive job markets. Industry variations are also notable, with finance and tech industries often offering higher compensation compared to other sectors. Company size can influence salary composition as well, with larger companies typically offering more comprehensive benefits and stock options.

Increasing Salary

To increase your salary further from this position, consider the following strategies:

• Skill Enhancement: Continuously update and expand your skill set, particularly in emerging technologies and tools relevant to AI/ML and DevOps. Specializing in niche areas can make you more valuable.
• Leadership Roles: Transition into leadership or managerial roles, such as DevOps Manager or Director of Engineering, which typically come with higher compensation.
• Networking: Build a strong professional network within the industry. Networking can lead to opportunities in higher-paying companies or roles.
• Certifications: Obtain advanced certifications that are recognized in the industry, which can justify a higher salary.
• Negotiation: Improve your negotiation skills to better advocate for higher pay during performance reviews or when switching jobs.

Educational Requirements

Most Senior-level/Expert DevOps Engineer positions in AI/ML/Data Science require at least a bachelor's degree in computer science, engineering, information technology, or a related field. However, a master's degree or even a Ph.D. can be advantageous, particularly in roles that require a deep understanding of AI/ML concepts. Advanced degrees can also provide a competitive edge and potentially lead to higher salaries.

Helpful Certifications

Several certifications can be beneficial for a Senior-level/Expert DevOps Engineer in AI/ML/Data Science:

• AWS Certified DevOps Engineer: Demonstrates expertise in managing and operating systems on AWS.
• Google Professional DevOps Engineer: Validates skills in building software delivery pipelines, deploying and monitoring services, and managing incidents.
• Certified Kubernetes Administrator (CKA): Shows proficiency in managing Kubernetes clusters, which is crucial for modern DevOps roles.
• Microsoft Certified: Azure DevOps Engineer Expert: Highlights skills in designing and implementing DevOps practices using Microsoft Azure.
• Certified Machine Learning Professional (CMLP): While not strictly a DevOps certification, it can be valuable for roles that intersect with AI/ML.

Required Experience

Typically, a Senior-level/Expert DevOps Engineer role requires at least 5 to 10 years of experience in software development, systems administration, or IT operations, with a significant portion of that time spent in DevOps roles. Experience with cloud platforms, CI/CD pipelines, containerization, and orchestration tools is often essential. Additionally, experience in AI/ML environments, such as deploying machine learning models or managing data pipelines, can be highly beneficial.

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