Data Management Specialist Salary in 2024

Salary details

The average Data Management Specialist salary lies between USD 65,000 and USD 116,100 globally. 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.

All data shown are full-time equivalent (FTE) salaries. Part-time salary information has been extrapolated to its FTE value.

Last updated: Dec. 15, 2024

Top 20 Job Tags for Data Management Specialist roles

The three most common job tag items assiciated with Data Management Specialist job listings are Data management, Excel and Data quality. 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 | 265 jobs Excel | 96 jobs Data quality | 84 jobs Security | 71 jobs Engineering | 66 jobs Research | 62 jobs SQL | 61 jobs Data governance | 50 jobs Statistics | 46 jobs Computer Science | 42 jobs Testing | 40 jobs Finance | 38 jobs Python | 37 jobs Data analysis | 34 jobs Mathematics | 33 jobs Privacy | 30 jobs R | 29 jobs Power BI | 28 jobs Agile | 28 jobs Pharma | 26 jobs

Top 20 Job Perks/Benefits for Data Management Specialist roles

The three most common job benefits and perks assiciated with Data Management Specialist 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 | 115 jobs Health care | 97 jobs Flex hours | 76 jobs Team events | 56 jobs Competitive pay | 49 jobs Salary bonus | 44 jobs Insurance | 40 jobs Medical leave | 38 jobs Equity / stock options | 36 jobs Flex vacation | 33 jobs Parental leave | 31 jobs Startup environment | 27 jobs Relocation support | 22 jobs Wellness | 20 jobs 401(k) matching | 17 jobs Transparency | 11 jobs Flexible spending account | 11 jobs Gear | 7 jobs Fitness / gym | 4 jobs Home office stipend | 4 jobs

Salary Composition

The salary for a Data Management Specialist in AI/ML/Data Science typically comprises a fixed base salary, performance bonuses, and additional remuneration such as stock options or benefits. The fixed base salary is the most significant component, often accounting for 70-85% of the total compensation package. Performance bonuses can vary widely, ranging from 5-20% of the base salary, depending on the company's performance and individual achievements. Additional remuneration, such as stock options, profit-sharing, or comprehensive benefits packages, can make up the remaining 5-15%.

Regional differences play a significant role in salary composition. For instance, positions in tech hubs like Silicon Valley or New York City may offer higher base salaries and stock options due to the high cost of living and competitive job market. Industry also influences salary composition; tech companies might offer more in stock options, while financial firms might provide higher cash bonuses. Company size can affect the package as well, with larger companies often providing more comprehensive benefits and smaller startups offering equity as a significant part of the compensation.

Increasing Salary

To increase your salary from the position of a Data Management Specialist, consider the following strategies:

• Skill Enhancement: Continuously upgrade your technical skills, especially in emerging AI/ML technologies and tools. Proficiency in programming languages like Python, R, and SQL, as well as experience with data visualization tools, can make you more valuable.

• Advanced Education: Pursuing a master's degree or specialized certifications in data science or AI can position you for higher-paying roles.

• Networking: Engage with professional networks and communities in AI/ML to learn about new opportunities and trends. Attending conferences and workshops can also open doors to higher-paying positions.

• Leadership Roles: Aim for leadership or managerial roles within your organization. These positions often come with higher salaries and additional responsibilities.

• Industry Transition: Consider moving to industries that pay higher salaries for data management roles, such as finance, healthcare, or tech.

Educational Requirements

Most Data Management Specialist roles require at least a bachelor's degree in a related field such as Computer Science, Information Technology, Data Science, or Statistics. However, a master's degree can be highly advantageous and is often preferred by employers, especially for roles that involve complex data analysis and strategic decision-making. Advanced degrees can also provide a competitive edge and open up opportunities for higher-level positions.

Helpful Certifications

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

• Certified Data Management Professional (CDMP): This certification is widely recognized and covers essential data management skills.

• Microsoft Certified: Azure Data Scientist Associate: Useful for those working with Microsoft's cloud services and data solutions.

• Google Professional Data Engineer: Ideal for professionals working with Google's cloud platform.

• AWS Certified Data Analytics – Specialty: Beneficial for those using Amazon Web Services for data analytics.

• Certified Analytics Professional (CAP): A general certification that validates your ability to transform data into valuable insights.

Experience Requirements

Typically, a Data Management Specialist role requires 3-5 years of experience in data management, data analysis, or a related field. Experience with data warehousing, database management, and data governance is often essential. Familiarity with AI/ML tools and frameworks, as well as experience in handling large datasets, can be crucial. Employers also value experience in specific industries, as domain knowledge can significantly enhance your ability to manage and interpret data effectively.

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