Data Operations Engineer Salary in United States during 2023
💰 The median Data Operations Engineer Salary in United States during 2023 is USD 192,700
✏️ This salary info is based on 8 individual salaries reported during 2023
Salary details
The average Data Operations Engineer salary lies between USD 125,000 and USD 238,530 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.
- Job title
- Data Operations Engineer
- Experience
- all levels
- Region
- United States
- Salary year
- 2023
- Sample size
- 8
- Top 10%
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- Top 25%
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- Median
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- Bottom 25%
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- Bottom 10%
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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:Salary trend
Top 20 Job Tags for Data Operations Engineer roles
The three most common job tag items assiciated with Data Operations Engineer job listings are DataOps, Python and SQL. Below you find a list of the 20 most occuring job tags in 2023 and the number of open jobs that where associated with them during that period:
DataOps | 25 jobs Python | 22 jobs SQL | 20 jobs AWS | 19 jobs Engineering | 18 jobs Computer Science | 15 jobs ETL | 13 jobs Pipelines | 12 jobs Machine Learning | 11 jobs Architecture | 11 jobs Privacy | 11 jobs Security | 10 jobs GCP | 9 jobs Snowflake | 9 jobs Hadoop | 8 jobs Spark | 8 jobs Research | 8 jobs Testing | 8 jobs Data analysis | 8 jobs Data pipelines | 7 jobsTop 20 Job Perks/Benefits for Data Operations Engineer roles
The three most common job benefits and perks assiciated with Data Operations Engineer job listings are Career development, Health care and Flex vacation. Below you find a list of the 20 most occuring job perks or benefits in 2023 and the number of open jobs that where offering them during that period:
Career development | 20 jobs Health care | 16 jobs Flex vacation | 13 jobs Parental leave | 12 jobs Flex hours | 12 jobs Startup environment | 11 jobs Equity / stock options | 9 jobs Medical leave | 9 jobs Insurance | 9 jobs 401(k) matching | 8 jobs Competitive pay | 8 jobs Team events | 8 jobs Home office stipend | 7 jobs Salary bonus | 4 jobs Wellness | 3 jobs Fitness / gym | 3 jobs Fertility benefits | 2 jobs Lunch / meals | 1 jobs Transparency | 1 jobs Snacks / Drinks | 1 jobsSalary Composition
The salary for a Data Operations Engineer in the AI/ML/Data Science field typically comprises several components. The fixed base salary is the largest portion, often accounting for 70-80% of the total compensation package. Bonuses, which can be performance-based or company-wide, usually make up 10-20% of the salary. Additional remuneration might include stock options, especially in tech companies, and other benefits like health insurance, retirement contributions, and paid time off. The exact composition can vary significantly depending on the region, industry, and company size. For instance, tech giants in Silicon Valley might offer substantial stock options, while smaller companies might focus more on cash bonuses.
Increasing Salary
To increase your salary from the position of a Data Operations Engineer, consider the following steps:
- Skill Enhancement: Continuously update your skills in emerging technologies and tools relevant to AI/ML and data science.
- Advanced Education: Pursue advanced degrees or specialized certifications that can set you apart.
- Leadership Roles: Aim for leadership or managerial roles that come with higher pay scales.
- Industry Switch: Consider moving to industries that pay higher for data operations roles, such as finance or healthcare.
- Networking: Build a strong professional network to learn about higher-paying opportunities.
Educational Requirements
Most Data Operations Engineers hold at least a bachelor's degree in computer science, information technology, data science, or a related field. A master's degree can be advantageous and is often preferred by top-tier companies. Courses in statistics, mathematics, and engineering can also be beneficial, as they provide a strong foundation for understanding complex data systems.
Helpful Certifications
Several certifications can enhance your credentials as a Data Operations Engineer:
- Certified Data Management Professional (CDMP)
- AWS Certified Data Analytics
- Google Professional Data Engineer
- Microsoft Certified: Azure Data Engineer Associate
These certifications demonstrate expertise in data management and cloud platforms, which are crucial for data operations roles.
Required Experience
Typically, a Data Operations Engineer is expected to have 3-5 years of experience in data management, data engineering, or a related field. Experience with data warehousing, ETL processes, and cloud platforms is often required. Familiarity with programming languages like Python, SQL, and tools like Hadoop or Spark is also essential.
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