Salary for Senior-level / Expert Data Analyst in United States during 2023

Salary details

The average senior-level / expert Data Analyst salary lies between USD 93,919 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: Nov. 24, 2024

Salary trend

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

The three most common job tag items assiciated with senior-level / expert Data Analyst job listings are SQL, Python and Tableau. 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:

SQL | 1947 jobs Python | 1386 jobs Tableau | 1187 jobs Statistics | 1121 jobs Data analysis | 950 jobs Engineering | 904 jobs R | 787 jobs Power BI | 756 jobs Excel | 740 jobs Data visualization | 678 jobs Data Analytics | 660 jobs Mathematics | 642 jobs Computer Science | 589 jobs Looker | 563 jobs Finance | 551 jobs Research | 494 jobs Business Intelligence | 494 jobs Machine Learning | 444 jobs KPIs | 443 jobs Testing | 442 jobs

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

The three most common job benefits and perks assiciated with senior-level / expert Data Analyst job listings are Career development, Health care and Startup environment. 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 | 1394 jobs Health care | 872 jobs Startup environment | 663 jobs Flex hours | 653 jobs Equity / stock options | 566 jobs Competitive pay | 536 jobs Flex vacation | 520 jobs Team events | 469 jobs Salary bonus | 437 jobs Insurance | 416 jobs Parental leave | 408 jobs Medical leave | 297 jobs 401(k) matching | 256 jobs Wellness | 227 jobs Home office stipend | 182 jobs Fitness / gym | 169 jobs Gear | 142 jobs Unlimited paid time off | 142 jobs Relocation support | 69 jobs Transparency | 61 jobs

Salary Composition

In the United States, the salary composition for a Senior-level or Expert Data Analyst in AI/ML/Data Science typically includes a mix of base salary, bonuses, and additional remuneration such as stock options or profit-sharing. The base salary often constitutes the largest portion, ranging from 70% to 85% of the total compensation package. Bonuses, which can be performance-based or company-wide, usually account for 10% to 20%. Additional remuneration, such as stock options, profit-sharing, or other incentives, can make up the remaining 5% to 10%.

The composition can vary significantly depending on the region, industry, and company size. For instance, tech hubs like San Francisco or New York may offer higher base salaries and stock options, while companies in the Midwest might provide more substantial bonuses. Larger companies often have more structured bonus programs and stock options, whereas smaller startups might offer equity as a significant part of the compensation package.

Increasing Salary

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

• Skill Enhancement: Continuously update your skills in emerging technologies and tools in AI/ML and data science. Specializing in niche areas like deep learning, natural language processing, or big data analytics can make you more valuable.

• Advanced Education: Pursuing advanced degrees such as a Master's or Ph.D. in Data Science, Computer Science, or a related field can open doors to higher-paying roles.

• Leadership Roles: Transitioning into leadership or managerial roles, such as a Data Science Manager or Director of Analytics, can significantly increase your earning potential.

• Industry Change: Some industries, like finance or healthcare, may offer higher salaries for data analysts due to the complexity and critical nature of the data.

• Networking and Visibility: Building a strong professional network and increasing your visibility in the field through speaking engagements, publications, or contributions to open-source projects can lead to better job offers.

Educational Requirements

Most Senior-level Data Analyst positions require at least a bachelor's degree in a relevant field such as Data Science, Computer Science, Statistics, Mathematics, or Engineering. However, a master's degree is often preferred and can be a significant advantage. Some roles may even require a Ph.D., especially in research-intensive positions or at top-tier companies.

Helpful Certifications

While not always mandatory, certain certifications can enhance your credentials and demonstrate your expertise:

• Certified Analytics Professional (CAP): This certification is widely recognized and covers a broad range of analytics skills.

• Google Professional Data Engineer: This certification is beneficial for those working with Google Cloud Platform and data engineering tasks.

• Microsoft Certified: Azure Data Scientist Associate: Useful for professionals working with Azure and machine learning models.

• AWS Certified Machine Learning – Specialty: Ideal for those working with AWS and machine learning.

• SAS Certified Data Scientist: Focuses on using SAS tools for data analysis and machine learning.

Experience Requirements

Typically, a Senior-level Data Analyst position requires at least 5 to 8 years of experience in data analysis or a related field. This experience should include hands-on work with data analytics tools and technologies, as well as a proven track record of delivering insights and solutions that drive business decisions. Experience in leading projects or teams can also be crucial for senior roles.

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