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

Salary details

The average senior-level / expert DataOps Engineer salary lies between USD 73,008 and USD 170,640 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. 17, 2024

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

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

DataOps | 33 jobs Python | 29 jobs Pipelines | 25 jobs SQL | 22 jobs Terraform | 22 jobs AWS | 21 jobs Engineering | 21 jobs Security | 21 jobs Data pipelines | 21 jobs DevOps | 20 jobs CI/CD | 17 jobs Kubernetes | 16 jobs ETL | 13 jobs Architecture | 13 jobs Snowflake | 13 jobs Kafka | 12 jobs Computer Science | 12 jobs Big Data | 11 jobs Airflow | 11 jobs PostgreSQL | 11 jobs

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

The three most common job benefits and perks assiciated with senior-level / expert DataOps Engineer job listings are Career development, Flex hours and Team events. 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 | 19 jobs Flex hours | 12 jobs Team events | 11 jobs Equity / stock options | 9 jobs Parental leave | 9 jobs Health care | 9 jobs Startup environment | 8 jobs Salary bonus | 8 jobs Competitive pay | 6 jobs Insurance | 6 jobs Flex vacation | 5 jobs Medical leave | 4 jobs Gear | 3 jobs Home office stipend | 2 jobs Paid sabbatical | 2 jobs 401(k) matching | 1 jobs Fitness / gym | 1 jobs Transparency | 1 jobs Signing bonus | 1 jobs Relocation support | 1 jobs

Salary Composition for a Senior DataOps Engineer

The salary for a Senior DataOps Engineer in the United States typically comprises a base salary, performance bonuses, and additional remuneration such as stock options or profit-sharing. The base salary often constitutes the majority of the total compensation package, ranging from 70% to 85%. Performance bonuses can vary significantly depending on the company and industry, usually accounting for 10% to 20% of the total compensation. Additional remuneration, such as stock options, profit-sharing, or other incentives, can make up the remaining 5% to 10%.

Regional differences can also impact salary composition. For instance, tech hubs like Silicon Valley or New York City may offer higher base salaries and more substantial stock options due to the competitive job market. In contrast, companies in smaller cities or regions with a lower cost of living might offer a higher percentage of performance bonuses to attract talent. Industry and company size also play a role; larger tech companies or those in high-demand sectors like finance or healthcare may provide more lucrative stock options or bonuses compared to smaller startups.

Steps to Increase Salary from a Senior DataOps Engineer Position

To increase your salary beyond the median for a Senior DataOps Engineer, consider the following strategies:

• Specialize in High-Demand Skills: Focus on acquiring expertise in niche areas within DataOps, such as cloud-native data solutions, machine learning operations (MLOps), or data security. These skills are in high demand and can command higher salaries.

• Pursue Leadership Roles: Transitioning into a leadership or managerial role, such as a DataOps Manager or Director, can significantly increase your earning potential. This often involves developing strong project management and team leadership skills.

• Expand Your Network: Engage with professional networks and communities in the AI/ML and Data Science fields. Networking can lead to opportunities in higher-paying companies or industries.

• Negotiate Effectively: When offered a new position or during performance reviews, negotiate for higher compensation by highlighting your unique skills, achievements, and contributions to the company.

Educational Requirements for a Senior DataOps Engineer

Most Senior DataOps Engineer positions require at least a bachelor's degree in a relevant field such as Computer Science, Information Technology, or Data Science. However, a master's degree or higher in these fields can be advantageous and may be preferred by some employers. Advanced degrees can provide a deeper understanding of complex data systems and analytics, which is beneficial for senior-level roles.

Helpful Certifications for a Senior DataOps Engineer

Certifications can enhance your credentials and demonstrate expertise in specific areas. Some valuable certifications for a Senior DataOps Engineer include:

• Certified Kubernetes Administrator (CKA): Demonstrates proficiency in managing Kubernetes clusters, which is crucial for DataOps in cloud environments.
• AWS Certified Solutions Architect: Validates skills in designing and deploying scalable systems on AWS, a common platform for DataOps.
• Google Professional Data Engineer: Focuses on designing, building, and operationalizing data processing systems on Google Cloud Platform.
• Certified Data Management Professional (CDMP): Offers a broad understanding of data management principles and practices.

Experience Required for a Senior DataOps Engineer

Typically, a Senior DataOps Engineer is expected to have 5 to 10 years of experience in data engineering, DevOps, or related fields. This experience should include hands-on work with data pipelines, cloud platforms, and automation tools. Experience in leading projects or teams, as well as a track record of implementing successful data solutions, is often required for senior-level positions.

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