Data Operations Engineer Salary in 2023

Salary details

The average Data Operations Engineer salary lies between USD 125,000 and USD 238,530 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. 10, 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 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 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, 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 jobs

Salary Composition

The salary for a Data Operations Engineer in AI/ML/Data Science typically comprises a fixed base salary, performance-based bonuses, and additional remuneration such as stock options or benefits. The composition can vary significantly depending on the region, industry, and company size. In tech hubs like Silicon Valley, the base salary might be higher, but bonuses and stock options can also form a substantial part of the total compensation package. In contrast, companies in smaller markets or industries like healthcare or finance might offer a lower base salary but compensate with higher bonuses or benefits. Larger companies often provide more comprehensive benefits and stock options, while startups might offer equity as a significant part of the package to attract talent.

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 courses in data science, machine learning, or related fields.
• Leadership Roles: Aim for leadership or managerial roles that come with higher responsibilities and pay.
• Industry Switch: Consider moving to industries that pay higher salaries for similar roles, such as finance or tech.
• Networking: Build a strong professional network to learn about higher-paying opportunities and negotiate better offers.

Educational Requirements

Most Data Operations Engineer roles require at least a bachelor's degree in computer science, data science, engineering, or a related field. However, a master's degree or Ph.D. can be advantageous, especially for roles that demand a deeper understanding of machine learning algorithms and data analysis techniques. Some positions might also require coursework in statistics, mathematics, or business analytics.

Helpful Certifications

Certifications can enhance your credentials and demonstrate your expertise to potential employers. Some valuable certifications include:

• Certified Data Professional (CDP)
• Google Professional Data Engineer
• AWS Certified Big Data – Specialty
• Microsoft Certified: Azure Data Scientist Associate
• Cloudera Certified Data Engineer

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

Required Experience

Typically, a Data Operations Engineer role requires 3-5 years of experience in data engineering, data analysis, or a related field. Experience with data warehousing, ETL processes, and cloud platforms is often essential. Familiarity with programming languages like Python, R, or SQL, and tools like Hadoop, Spark, or Tableau, is also commonly required. Experience in managing data pipelines and working with large datasets is highly valued.

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