Salary for Senior-level / Expert Data Operations Analyst in United States during 2024

Salary details

The average senior-level / expert Data Operations Analyst salary lies between USD 90,000 and USD 145,000 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: Dec. 22, 2024

Top 20 Job Tags for Senior-level / Expert Data Operations Analyst roles

The three most common job tag items assiciated with senior-level / expert Data Operations Analyst job listings are DataOps, SQL and Security. 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 | 21 jobs SQL | 18 jobs Security | 11 jobs Data management | 11 jobs Tableau | 9 jobs Research | 9 jobs Excel | 9 jobs Data analysis | 9 jobs Computer Science | 9 jobs Data quality | 9 jobs Engineering | 7 jobs Python | 6 jobs Finance | 6 jobs Agile | 6 jobs Mathematics | 6 jobs Data governance | 6 jobs AWS | 5 jobs Architecture | 5 jobs Data visualization | 5 jobs Statistics | 5 jobs

Top 20 Job Perks/Benefits for Senior-level / Expert Data Operations Analyst roles

The three most common job benefits and perks assiciated with senior-level / expert Data Operations Analyst job listings are Career development, Health care 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 | 15 jobs Health care | 11 jobs Team events | 7 jobs Salary bonus | 7 jobs Flex hours | 6 jobs Startup environment | 6 jobs Competitive pay | 6 jobs Equity / stock options | 5 jobs 401(k) matching | 4 jobs Flex vacation | 4 jobs Insurance | 4 jobs Home office stipend | 4 jobs Wellness | 3 jobs Fitness / gym | 2 jobs Flexible spending account | 2 jobs Transparency | 1 jobs Unlimited paid time off | 1 jobs

Salary Composition for a Senior Data Operations Analyst

The salary for a Senior Data Operations Analyst 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, usually around 70-80%. Performance bonuses can range from 10-20% of the total salary, depending on the company's performance and individual achievements. Additional remuneration, such as stock options or profit-sharing, can vary significantly based on the company's size and industry. For instance, tech companies or startups might offer more in stock options, while larger, established firms might provide more in terms of bonuses. Regional differences also play a role; for example, salaries in tech hubs like San Francisco or New York City might be higher due to the cost of living and demand for skilled professionals.

Steps to Increase Salary from This Position

To increase your salary from a Senior Data Operations Analyst position, consider the following strategies:

• Skill Enhancement: Continuously update your skills in emerging technologies and tools relevant to AI/ML and data science. This could include learning new programming languages, mastering advanced data analytics tools, or gaining expertise in machine learning algorithms.

• Advanced Education: Pursuing further education, such as a master's degree or a Ph.D. in data science, computer science, or a related field, can open doors to higher-paying roles.

• Leadership Roles: Aim for leadership or managerial positions within your organization. Roles such as Data Science Manager or Director of Data Operations typically come with higher salaries.

• Industry Transition: Consider transitioning to industries that offer higher pay for data professionals, such as finance, healthcare, or technology.

• Networking and Professional Visibility: Attend industry conferences, contribute to open-source projects, or publish research to increase your visibility and network within the industry.

Educational Requirements

Most Senior Data Operations Analyst positions require at least a bachelor's degree in a relevant field such as computer science, data science, statistics, or mathematics. However, many employers prefer candidates with a master's degree or higher, especially for senior-level roles. Advanced degrees provide a deeper understanding of complex data analysis techniques and machine learning algorithms, which are crucial for this position.

Helpful Certifications

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

• Certified Analytics Professional (CAP): This certification demonstrates your ability to transform data into valuable insights and is recognized across various industries.

• Google Professional Data Engineer: This certification focuses on designing, building, and operationalizing data processing systems, which is highly relevant for data operations roles.

• AWS Certified Data Analytics – Specialty: This certification is beneficial if you work with AWS services and want to validate your expertise in data analytics.

• Microsoft Certified: Azure Data Scientist Associate: This certification is useful if you work with Azure and want to showcase your skills in applying data science techniques.

Required Experience

Typically, a Senior Data Operations Analyst is expected to have at least 5-7 years of experience in data analysis, data engineering, or a related field. This experience should include a strong background in data management, statistical analysis, and the use of data analytics tools and programming languages such as Python, R, or SQL. Experience in leading projects or teams can also be advantageous, as it demonstrates your ability to manage complex data operations and collaborate effectively with cross-functional teams.

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