Data Operations Engineer Salary in 2022

Salary details

The average Data Operations Engineer salary lies between USD 60,000 and USD 100,000 globally. 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.

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

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 Python, Engineering and SQL. Below you find a list of the 20 most occuring job tags in 2022 and the number of open jobs that where associated with them during that period:

Python | 17 jobs Engineering | 14 jobs SQL | 13 jobs DataOps | 11 jobs AWS | 8 jobs Research | 8 jobs Testing | 8 jobs Pipelines | 8 jobs Security | 7 jobs Architecture | 7 jobs Agile | 7 jobs APIs | 7 jobs NoSQL | 6 jobs Data pipelines | 6 jobs PostgreSQL | 6 jobs ETL | 5 jobs Hadoop | 5 jobs Redshift | 5 jobs Airflow | 5 jobs Statistics | 5 jobs

Top 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, Startup environment and Equity / stock options. Below you find a list of the 20 most occuring job perks or benefits in 2022 and the number of open jobs that where offering them during that period:

Career development | 11 jobs Startup environment | 9 jobs Equity / stock options | 8 jobs Flex hours | 8 jobs Flex vacation | 7 jobs Health care | 6 jobs Parental leave | 5 jobs Competitive pay | 5 jobs Team events | 4 jobs Medical leave | 4 jobs 401(k) matching | 3 jobs Salary bonus | 3 jobs Lunch / meals | 1 jobs Wellness | 1 jobs Fitness / gym | 1 jobs Transparency | 1 jobs Insurance | 1 jobs Unlimited paid time off | 1 jobs Fertility benefits | 1 jobs

Salary Composition

The salary composition for a Data Operations Engineer can vary significantly based on factors such as region, industry, and company size. Typically, the salary is divided into a fixed base salary, performance bonuses, and additional remuneration such as stock options or benefits. In regions with a high cost of living, like the San Francisco Bay Area or New York City, the base salary might be higher to compensate for living expenses. In contrast, regions with a lower cost of living might offer a lower base salary but could compensate with more substantial bonuses or stock options. Industries such as finance or tech often provide higher bonuses compared to non-profit or public sector roles. Larger companies might offer more comprehensive benefits packages, including health insurance, retirement plans, and professional development funds, which can add significant value to the overall compensation package.

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 AI/ML and data science. Learning new programming languages, tools, or frameworks can make you more valuable.
• Advanced Education: Pursuing a master's degree or specialized certifications can open up higher-paying opportunities.
• Networking: Engage with industry professionals through conferences, meetups, or online platforms like LinkedIn. Networking can lead to new job opportunities or promotions.
• Performance Excellence: Consistently exceed performance expectations in your current role to position yourself for raises or promotions.
• Negotiation: When offered a new position or during performance reviews, negotiate for a higher salary based on your skills and market research.

Educational Requirements

Most Data Operations Engineer positions require at least a bachelor's degree in a related field such as computer science, information technology, or data science. Some roles may prefer or require a master's degree, especially for positions that involve more complex data analysis or machine learning tasks. A strong foundation in mathematics, statistics, and programming is essential, as these skills are critical for handling data operations effectively.

Helpful Certifications

Certifications can enhance your qualifications and demonstrate your expertise to potential employers. Some valuable certifications for a Data Operations Engineer include:

• Certified Data Management Professional (CDMP)
• AWS Certified Data Analytics
• Google Professional Data Engineer
• Microsoft Certified: Azure Data Engineer Associate
• Cloudera Certified Data Analyst

These certifications can validate your skills in data management, cloud platforms, and data engineering, making you a more competitive candidate.

Experience Requirements

Typically, a Data Operations Engineer role requires 2-5 years of experience in data-related positions. Experience in data management, data warehousing, or data engineering is often necessary. Familiarity with data processing tools, cloud platforms, and programming languages like Python, SQL, or R is usually expected. Experience with big data technologies such as Hadoop or Spark can also be advantageous.

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