Salary for Executive-level / Director Data Analyst in United States during 2023

Salary details

The average executive-level / director Data Analyst salary lies between USD 70,000 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: Nov. 24, 2024

Salary trend

Top 20 Job Tags for Executive-level / Director Data Analyst roles

The three most common job tag items assiciated with executive-level / director Data Analyst job listings are SQL, Statistics and Excel. 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 | 25 jobs Statistics | 19 jobs Excel | 17 jobs Python | 16 jobs Tableau | 15 jobs Data analysis | 15 jobs Data Analytics | 13 jobs Power BI | 13 jobs R | 12 jobs Engineering | 12 jobs Research | 11 jobs Economics | 11 jobs Mathematics | 11 jobs Privacy | 11 jobs KPIs | 8 jobs Looker | 7 jobs Business Intelligence | 7 jobs Security | 7 jobs Data visualization | 7 jobs Computer Science | 7 jobs

Top 20 Job Perks/Benefits for Executive-level / Director Data Analyst roles

The three most common job benefits and perks assiciated with executive-level / director Data Analyst job listings are Health care, Career development 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:

Health care | 21 jobs Career development | 21 jobs Startup environment | 12 jobs Flex hours | 10 jobs Competitive pay | 9 jobs Medical leave | 9 jobs Equity / stock options | 8 jobs Wellness | 6 jobs Team events | 6 jobs Salary bonus | 5 jobs Flex vacation | 4 jobs Insurance | 4 jobs Parental leave | 3 jobs Relocation support | 3 jobs Yoga | 2 jobs Unlimited paid time off | 2 jobs 401(k) matching | 1 jobs Travel | 1 jobs Gear | 1 jobs Home office stipend | 1 jobs

Salary Composition

The salary for an Executive-level or Director Data Analyst in the AI/ML/Data Science field typically comprises a base salary, bonuses, and additional remuneration such as stock options or benefits. The base salary is the fixed component and usually forms the largest part of the total compensation package. Bonuses can vary significantly depending on the company's performance, individual performance, and industry standards. In tech-heavy regions like Silicon Valley, bonuses and stock options might be more substantial compared to other areas. Additionally, larger companies or those in high-demand industries like tech or finance may offer more competitive compensation packages, including higher bonuses and more lucrative stock options.

Increasing Salary

To increase your salary from this position, consider the following strategies:

• Skill Enhancement: Continuously update your skills in emerging technologies and methodologies in AI/ML and data science. Specializing in niche areas can make you more valuable.
• Networking: Build a strong professional network. Engaging with industry leaders and attending conferences can open up higher-paying opportunities.
• Leadership Roles: Aim for roles with more responsibility, such as leading larger teams or managing cross-functional projects.
• Industry Shift: Consider moving to industries that pay higher salaries for data science roles, such as finance, healthcare, or tech.
• Advanced Education: Pursuing further education, such as an MBA or a specialized master's degree, can position you for higher-level roles.

Educational Requirements

Most executive-level data analyst roles require at least a bachelor's degree in a relevant field such as computer science, statistics, mathematics, or engineering. However, a master's degree or Ph.D. is often preferred, especially in competitive markets. Advanced degrees in data science, business analytics, or a related field can provide a significant advantage.

Helpful Certifications

While not always mandatory, certain certifications can enhance your credibility and skill set:

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

Required Experience

Typically, a minimum of 8-10 years of experience in data analysis, data science, or a related field is required for executive-level roles. This experience should include a proven track record of leading data-driven projects, managing teams, and delivering actionable insights that drive business decisions. Experience in strategic planning and a deep understanding of industry-specific data challenges are also crucial.

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