Salary for Mid-level / Intermediate Data Operations Engineer during 2022

Salary details

The average mid-level / intermediate 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 Mid-level / Intermediate Data Operations Engineer roles

The three most common job tag items assiciated with mid-level / intermediate Data Operations Engineer job listings are Python, SQL and Airflow. 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 | 4 jobs SQL | 4 jobs Airflow | 4 jobs Engineering | 4 jobs Testing | 4 jobs Architecture | 3 jobs Statistics | 3 jobs Mathematics | 3 jobs STEM | 3 jobs DataOps | 3 jobs Privacy | 3 jobs Machine Learning | 1 jobs ETL | 1 jobs Research | 1 jobs Consulting | 1 jobs Banking | 1 jobs Finance | 1 jobs Data pipelines | 1 jobs FinTech | 1 jobs Data strategy | 1 jobs

Top 20 Job Perks/Benefits for Mid-level / Intermediate Data Operations Engineer roles

The three most common job benefits and perks assiciated with mid-level / intermediate Data Operations Engineer job listings are Equity / stock options, Flex hours and Flex vacation. 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:

Equity / stock options | 4 jobs Flex hours | 4 jobs Flex vacation | 4 jobs Career development | 4 jobs Competitive pay | 4 jobs Parental leave | 3 jobs Health care | 3 jobs Medical leave | 3 jobs Salary bonus | 3 jobs Startup environment | 1 jobs Team events | 1 jobs

Salary Composition

The salary composition for a Mid-level Data Operations Engineer can vary significantly based on factors such as region, industry, and company size. Typically, the salary is divided into three main components: base salary, bonuses, and additional remuneration.

• Base Salary: This is the fixed annual amount and usually constitutes the largest portion of the total compensation package. In regions with a high cost of living, such as the San Francisco Bay Area or New York City, the base salary might be higher to compensate for living expenses.

• Bonuses: These are performance-based and can vary widely. In tech-heavy industries or larger companies, bonuses might be more substantial, often tied to individual performance, team success, or company profitability.

• Additional Remuneration: This can include stock options, profit-sharing, or other incentives. Tech companies, especially startups, often offer stock options as part of the compensation package to attract talent.

Increasing Salary

To increase your salary from a Mid-level Data Operations Engineer position, consider the following strategies:

• Skill Enhancement: Continuously update your skills in emerging technologies and tools relevant to AI/ML and data science. Proficiency in cloud platforms, big data technologies, and advanced analytics can make you more valuable.

• Advanced Education: Pursuing a master's degree or specialized certifications can enhance your qualifications and open up higher-paying opportunities.

• Networking: Engage with professional networks and communities. Building relationships with industry leaders can lead to new opportunities and insights into higher-paying roles.

• Performance and Negotiation: Consistently demonstrate high performance and be prepared to negotiate your salary during performance reviews or when taking on additional responsibilities.

Educational Requirements

Most Mid-level Data Operations Engineer positions require at least a bachelor's degree in a related field such as Computer Science, Information Technology, Data Science, or Engineering. Some employers may prefer candidates with a master's degree, especially for roles that involve more complex data operations and analysis.

Helpful Certifications

Certifications can bolster your credentials and demonstrate expertise in specific areas. Some valuable certifications include:

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

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

Required Experience

Typically, a Mid-level Data Operations Engineer is expected to have 3-5 years of experience in data operations, data engineering, or a related field. Experience with data pipelines, ETL processes, and data warehousing is often required. Familiarity with programming languages such as Python, SQL, and tools like Apache Spark or Hadoop is also beneficial.

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