Salary for Executive-level / Director Analytics Engineer in United States during 2022

Salary details

The average executive-level / director Analytics Engineer salary lies between USD 150,000 and USD 200,000 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 Analytics Engineer roles

The three most common job tag items assiciated with executive-level / director Analytics Engineer job listings are Engineering, SQL and Business Intelligence. 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:

Engineering | 6 jobs SQL | 5 jobs Business Intelligence | 5 jobs Python | 4 jobs AWS | 3 jobs Security | 3 jobs Privacy | 3 jobs Ruby | 2 jobs Machine Learning | 2 jobs Spark | 2 jobs Looker | 2 jobs Kubernetes | 2 jobs Data Analytics | 2 jobs Finance | 2 jobs BigQuery | 2 jobs SageMaker | 2 jobs GCP | 2 jobs Snowflake | 2 jobs Docker | 2 jobs Data governance | 2 jobs

Top 20 Job Perks/Benefits for Executive-level / Director Analytics Engineer roles

The three most common job benefits and perks assiciated with executive-level / director Analytics Engineer job listings are Health care, Career development and 401(k) matching. 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:

Health care | 5 jobs Career development | 5 jobs 401(k) matching | 4 jobs Equity / stock options | 4 jobs Flex vacation | 4 jobs Parental leave | 3 jobs Flex hours | 3 jobs Insurance | 3 jobs Unlimited paid time off | 3 jobs Startup environment | 2 jobs Lunch / meals | 1 jobs Wellness | 1 jobs Competitive pay | 1 jobs Team events | 1 jobs Medical leave | 1 jobs Fertility benefits | 1 jobs Paid sabbatical | 1 jobs

Salary Composition

The salary for an Executive-level or Director Analytics Engineer in the AI/ML/Data Science field typically comprises a base salary, performance bonuses, and additional remuneration such as stock options or equity, especially in tech companies. The base salary is often the largest component, accounting for approximately 70-80% of the total compensation package. Performance bonuses can vary significantly, ranging from 10-20% of the base salary, depending on the company's performance and individual achievements. Additional remuneration, such as stock options, can be a significant part of the package, particularly in startups or large tech firms, and may account for 10-20% of the total compensation.

Regional differences also play a role; for instance, salaries in tech hubs like Silicon Valley or New York City tend to be higher due to the cost of living and competitive job market. Industry-wise, tech companies, financial services, and healthcare often offer higher compensation packages compared to other sectors. Company size can also influence salary composition, with larger companies typically offering more comprehensive benefits and stock options.

Increasing Salary

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

• Skill Enhancement: Continuously update your technical skills and stay abreast of the latest trends in AI/ML and data science. Specializing in emerging areas like deep learning, natural language processing, or AI ethics can make you more valuable.

• Leadership Development: Enhance your leadership and management skills. Pursuing executive education programs or leadership certifications can prepare you for higher-level roles.

• Networking: Build a strong professional network within the industry. Attend conferences, join professional organizations, and engage in speaking opportunities to increase your visibility.

• Performance and Results: Demonstrate your impact through successful projects and measurable results. Documenting and communicating your achievements can position you for promotions or salary negotiations.

• Explore Opportunities: Consider opportunities in different regions or industries that may offer higher compensation. Sometimes, a lateral move to a company with a better compensation structure can be beneficial.

Educational Requirements

Most executive-level positions in AI/ML/Data Science require at least a bachelor's degree in a related field such as computer science, data science, statistics, or engineering. However, a master's degree or Ph.D. is often preferred, especially for roles that involve complex data analysis and strategic decision-making. Advanced degrees provide a deeper understanding of machine learning algorithms, data modeling, and statistical analysis, which are crucial for this role.

Helpful Certifications

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

• Certified Analytics Professional (CAP): Validates your ability to transform data into valuable insights.
• Google Professional Machine Learning Engineer: Demonstrates proficiency in designing, building, and productionizing ML models.
• AWS Certified Machine Learning – Specialty: Shows expertise in using AWS services for machine learning solutions.
• Data Science Certifications from Coursera or edX: Courses from reputable institutions can also be beneficial.

Experience Requirements

Typically, a minimum of 8-10 years of experience in data science, analytics, or a related field is required for an executive-level position. This experience should include a proven track record of leading data-driven projects, managing teams, and delivering business insights. Experience in strategic planning and decision-making is also crucial, as these roles often involve setting the direction for data initiatives within the organization.

Related salaries

Want to contribute?