Salary for Senior-level / Expert Data Analytics Manager in United States during 2024
💰 The median Salary for Senior-level / Expert Data Analytics Manager in United States during 2024 is USD 170,200
✏️ This salary info is based on 16 individual salaries reported during 2024
Salary details
The average senior-level / expert Data Analytics Manager salary lies between USD 135,800 and USD 235,000 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.
- Job title
- Data Analytics Manager
- Experience
- Senior-level / Expert
- Region
- United States
- Salary year
- 2024
- Sample size
- 16
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- Top 25%
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- Median
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- Bottom 25%
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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:Salary trend
Top 20 Job Tags for Senior-level / Expert Data Analytics Manager roles
The three most common job tag items assiciated with senior-level / expert Data Analytics Manager job listings are Data Analytics, SQL and Engineering. 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:
Data Analytics | 44 jobs SQL | 29 jobs Engineering | 24 jobs Tableau | 20 jobs Power BI | 18 jobs Python | 16 jobs R | 14 jobs Data analysis | 14 jobs Statistics | 13 jobs Machine Learning | 12 jobs Computer Science | 12 jobs Security | 9 jobs Architecture | 9 jobs Data visualization | 9 jobs Mathematics | 9 jobs Research | 8 jobs Finance | 8 jobs Excel | 8 jobs KPIs | 8 jobs Azure | 7 jobsTop 20 Job Perks/Benefits for Senior-level / Expert Data Analytics Manager roles
The three most common job benefits and perks assiciated with senior-level / expert Data Analytics Manager job listings are Career development, Startup environment and Health care. 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 | 24 jobs Startup environment | 16 jobs Health care | 15 jobs Flex hours | 12 jobs Flex vacation | 10 jobs Medical leave | 10 jobs Team events | 8 jobs Insurance | 8 jobs Equity / stock options | 7 jobs Parental leave | 7 jobs Competitive pay | 6 jobs Transparency | 4 jobs Wellness | 3 jobs Salary bonus | 3 jobs 401(k) matching | 2 jobs Fitness / gym | 2 jobs Relocation support | 2 jobs Unlimited paid time off | 2 jobs Signing bonus | 1 jobs Flexible spending account | 1 jobsSalary Composition
In the United States, the salary composition for a Senior-level or Expert Data Analytics Manager 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 60% to 80%. Bonuses can vary significantly depending on the company's performance and individual achievements, usually accounting for 10% to 20% of the total compensation. Additional remuneration, such as stock options, can make up the remaining 10% to 20%, particularly in larger tech firms or startups. The composition can vary by region, with tech hubs like Silicon Valley or New York offering higher base salaries and more substantial equity packages. Industry also plays a role; for instance, finance and tech sectors tend to offer more competitive compensation packages compared to other industries.
Next Steps for Salary Increase
To increase your salary further 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 like deep learning, natural language processing, or big data analytics can make you more valuable.
- Leadership Roles: Aim for roles with greater responsibility, such as Director of Data Science or Chief Data Officer, which typically come with higher compensation.
- Networking: Build a strong professional network within the industry. Attend conferences, join professional organizations, and engage in speaking opportunities to increase your visibility and open doors to higher-paying opportunities.
- Negotiation: When offered a new position or during performance reviews, negotiate for higher pay, emphasizing your contributions, skills, and market research on salary benchmarks.
Educational Requirements
Most Senior-level Data Analytics Manager positions 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 an MBA with a focus on analytics can provide a significant advantage and are sometimes necessary for higher-level roles.
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 Data Engineer: Focuses on designing, building, and operationalizing data processing systems.
- AWS Certified Big Data – Specialty: Demonstrates expertise in using AWS data lakes and analytics services.
- Microsoft Certified: Azure Data Scientist Associate: Validates your skills in applying data science techniques on Azure.
Required Experience
Typically, a Senior-level Data Analytics Manager is expected to have at least 7-10 years of experience in data analytics or related fields. This experience should include a proven track record of managing data projects, leading teams, and delivering actionable insights that drive business decisions. Experience in specific industries can also be beneficial, as it provides context and understanding of industry-specific challenges and data needs.
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