Salary for Senior-level / Expert Data Analyst in United States during 2022

Salary details

The average senior-level / expert Data Analyst salary lies between USD 99,000 and USD 135,000 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. 10, 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 2022 and the number of open jobs that where associated with them during that period:

SQL | 1351 jobs Python | 979 jobs Tableau | 872 jobs Engineering | 686 jobs Statistics | 676 jobs R | 588 jobs Data analysis | 575 jobs Looker | 491 jobs Finance | 480 jobs Data visualization | 456 jobs Excel | 445 jobs Data Analytics | 435 jobs Computer Science | 412 jobs Power BI | 359 jobs Business Intelligence | 356 jobs Mathematics | 346 jobs Testing | 344 jobs Machine Learning | 328 jobs Research | 326 jobs KPIs | 310 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 2022 and the number of open jobs that where offering them during that period:

Career development | 906 jobs Health care | 539 jobs Startup environment | 532 jobs Flex hours | 420 jobs Team events | 402 jobs Flex vacation | 353 jobs Competitive pay | 302 jobs Equity / stock options | 297 jobs Parental leave | 286 jobs Insurance | 209 jobs Salary bonus | 203 jobs Wellness | 179 jobs Medical leave | 167 jobs 401(k) matching | 158 jobs Home office stipend | 140 jobs Unlimited paid time off | 107 jobs Fitness / gym | 105 jobs Gear | 53 jobs Fertility benefits | 40 jobs Relocation support | 38 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 combination of a fixed base salary, performance-based bonuses, and additional remuneration such as stock options or equity, especially in tech companies. The base salary often constitutes the majority of the total compensation package, ranging from 70% to 85%. Bonuses can vary significantly depending on the company and industry, often ranging from 10% to 20% of the base salary. Additional remuneration, such as stock options, is more common in larger tech companies or startups and can add a significant amount to the total compensation, especially if the company performs well. Regional differences also play a role; for instance, salaries in tech hubs like San Francisco or New York City tend to be higher due to the cost of living and competitive job market.

Steps to Increase 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, such as deep learning frameworks, cloud computing, and big data technologies.
• Advanced Education: Pursuing a master's degree or Ph.D. in a related field can open up higher-paying opportunities.
• Leadership Roles: Transitioning into managerial or leadership roles, such as a Data Science Manager or Director, can significantly increase your earning potential.
• Industry Change: Moving to a higher-paying industry, such as finance or tech, can also result in a salary increase.
• Networking and Visibility: Building a strong professional network and increasing your visibility in the field through speaking engagements or publications can lead to better job offers.

Educational Requirements

Most Senior-level Data Analyst positions require at least a bachelor's degree in a related field such as Computer Science, Statistics, Mathematics, or Engineering. However, many employers prefer candidates with a master's degree or higher, especially for roles that involve complex data modeling and machine learning tasks. Advanced degrees can provide a deeper understanding of theoretical concepts and practical applications, which are crucial for high-level data analysis.

Helpful Certifications

While not always mandatory, certain certifications can enhance your credentials and demonstrate your expertise to potential employers. Some valuable certifications include:

• Certified Analytics Professional (CAP): Validates your ability to transform data into valuable insights.
• Microsoft Certified: Azure Data Scientist Associate: Focuses on using Azure's machine learning services.
• Google Professional Data Engineer: Demonstrates proficiency in designing and building data processing systems on Google Cloud.
• AWS Certified Machine Learning – Specialty: Validates expertise in building, training, and deploying machine learning models on AWS.

Required Experience

Typically, a Senior-level Data Analyst is expected to have at least 5 to 8 years of experience in data analysis or a related field. This experience should include a strong track record of working with large datasets, proficiency in data analysis tools and programming languages (such as Python, R, SQL), and experience with machine learning algorithms and statistical methods. Experience in leading projects or teams can also be beneficial.

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