Data Manager Salary in United States during 2024

Salary details

The average Data Manager salary lies between USD 78,000 and USD 130,000 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 Data Manager roles

The three most common job tag items assiciated with Data Manager job listings are Data management, Research and Excel. 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 management | 754 jobs Research | 388 jobs Excel | 369 jobs Data quality | 337 jobs SQL | 285 jobs Security | 248 jobs Statistics | 247 jobs Testing | 245 jobs Engineering | 234 jobs Data governance | 185 jobs Privacy | 185 jobs Pharma | 181 jobs Data analysis | 171 jobs Computer Science | 165 jobs SAS | 160 jobs CDISC | 160 jobs R | 154 jobs Python | 151 jobs Power BI | 145 jobs GCP | 128 jobs

Top 20 Job Perks/Benefits for Data Manager roles

The three most common job benefits and perks assiciated with Data Manager job listings are Career development, Health care and Flex hours. 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 | 583 jobs Health care | 358 jobs Flex hours | 283 jobs Team events | 255 jobs Competitive pay | 170 jobs Startup environment | 166 jobs Insurance | 166 jobs Equity / stock options | 135 jobs Salary bonus | 134 jobs Medical leave | 104 jobs Parental leave | 100 jobs Flex vacation | 100 jobs Wellness | 61 jobs 401(k) matching | 57 jobs Conferences | 37 jobs Relocation support | 32 jobs Gear | 25 jobs Travel | 23 jobs Home office stipend | 22 jobs Transparency | 19 jobs

Salary Composition for a Data Manager in AI/ML/Data Science

The salary composition for a Data Manager in AI/ML/Data Science can vary significantly based on factors such as region, industry, and company size. Typically, the salary is composed of a fixed base salary, which forms the bulk of the compensation package. In the United States, this base salary can range from 70% to 85% of the total compensation.

In addition to the base salary, bonuses are a common component, often tied to performance metrics or company profitability. These bonuses can range from 10% to 20% of the total compensation. Larger companies or those in high-demand industries like tech or finance may offer more substantial bonuses.

Additional remuneration might include stock options, especially in tech startups or publicly traded companies, and benefits such as health insurance, retirement contributions, and professional development allowances. The value of these additional benefits can vary widely but often constitutes 5% to 10% of the total compensation package.

Steps to Increase Salary from a Data Manager Position

To increase your salary from a Data Manager position, consider the following strategies:

• Skill Enhancement: Continuously update your skills in emerging technologies and methodologies in AI/ML and data science. This could involve learning new programming languages, mastering advanced data analytics tools, or gaining expertise in machine learning algorithms.

• Advanced Education: Pursuing further education, such as a master's degree or a Ph.D. in data science, computer science, or a related field, can make you more competitive for higher-paying roles.

• Leadership Roles: Seek opportunities to take on leadership roles or manage larger teams. Demonstrating your ability to lead projects and drive results can position you for promotions and salary increases.

• Industry Transition: Consider transitioning to industries that offer higher compensation for data management roles, such as finance, healthcare, or technology.

• Networking and Professional Visibility: Engage in networking opportunities, attend industry conferences, and contribute to professional forums. Building a strong professional network can lead to opportunities for higher-paying positions.

Educational Requirements for a Data Manager

Most Data Manager positions in AI/ML/Data Science require at least a bachelor's degree in a related field such as computer science, information technology, data science, or statistics. However, many employers prefer candidates with a master's degree, especially for roles that involve complex data analysis and strategic decision-making.

Helpful Certifications for Data Managers

Certifications can enhance your credentials and demonstrate your expertise to potential employers. Some valuable certifications for Data Managers include:

• Certified Data Management Professional (CDMP): Offered by DAMA International, this certification validates your knowledge and skills in data management.

• Microsoft Certified: Azure Data Scientist Associate: This certification is beneficial if you work with Microsoft's Azure platform and want to demonstrate your ability to apply data science techniques.

• AWS Certified Data Analytics – Specialty: Ideal for those working with Amazon Web Services, this certification showcases your ability to design and implement data analytics solutions.

• Google Professional Data Engineer: This certification is useful for those working with Google Cloud Platform and focuses on designing data processing systems.

Experience Requirements for a Data Manager

Typically, a Data Manager role requires several years of experience in data-related positions. This experience often includes:

• 3-5 years in data analysis, data engineering, or data management roles.
• Experience with data management tools and platforms, such as SQL, Hadoop, or Spark.
• Proven track record of managing data projects and leading data teams.

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