Salary for Mid-level / Intermediate Data Strategist during 2024
💰 The median Salary for Mid-level / Intermediate Data Strategist during 2024 is USD 130,000
✏️ This salary info is based on 24 individual salaries reported during 2024
Salary details
The average mid-level / intermediate Data Strategist salary lies between USD 100,000 and USD 170,185 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.
- Job title
- Data Strategist
- Experience
- Mid-level / Intermediate
- Region
- global/worldwide
- Salary year
- 2024
- Sample size
- 24
- Top 10%
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- Top 25%
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- Median
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- Bottom 25%
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- Bottom 10%
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All data shown are full-time equivalent (FTE) salaries. Part-time salary information has been extrapolated to its FTE value.
Last updated:Top 20 Job Tags for Mid-level / Intermediate Data Strategist roles
The three most common job tag items assiciated with mid-level / intermediate Data Strategist job listings are Engineering, Data strategy and SQL. 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:
Engineering | 12 jobs Data strategy | 10 jobs SQL | 9 jobs Architecture | 9 jobs Data management | 9 jobs Machine Learning | 8 jobs Data governance | 8 jobs Python | 7 jobs Research | 7 jobs Snowflake | 6 jobs Computer Science | 6 jobs Data quality | 6 jobs R | 5 jobs AWS | 5 jobs Data Analytics | 5 jobs Security | 5 jobs Testing | 5 jobs GCP | 5 jobs Databricks | 5 jobs Statistics | 5 jobsTop 20 Job Perks/Benefits for Mid-level / Intermediate Data Strategist roles
The three most common job benefits and perks assiciated with mid-level / intermediate Data Strategist job listings are Career development, Health care and Salary bonus. 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 | 17 jobs Health care | 11 jobs Salary bonus | 10 jobs Equity / stock options | 7 jobs Flex hours | 6 jobs Flex vacation | 6 jobs Competitive pay | 6 jobs Startup environment | 5 jobs Parental leave | 4 jobs Conferences | 4 jobs 401(k) matching | 3 jobs Medical leave | 3 jobs Insurance | 3 jobs Wellness | 2 jobs Team events | 2 jobs Home office stipend | 2 jobs Transparency | 1 jobs Signing bonus | 1 jobs Snacks / Drinks | 1 jobs Pet friendly | 1 jobsSalary Composition
The salary for a Mid-level/Intermediate Data Strategist typically comprises a base salary, performance bonuses, and additional remuneration such as stock options or benefits. The base salary is the fixed component and usually constitutes the majority of the total compensation package. Performance bonuses can vary significantly depending on the company's success and individual performance, often ranging from 10% to 20% of the base salary. Additional remuneration might include stock options, especially in tech companies, or benefits like health insurance, retirement plans, and professional development allowances.
Regional differences can affect salary composition, with tech hubs like San Francisco or New York offering higher base salaries but potentially lower relative bonuses due to the high cost of living. Industry also plays a role; for instance, finance and tech sectors might offer more lucrative bonuses compared to non-profit or public sectors. Company size can influence the availability of stock options, with larger companies more likely to offer them as part of the compensation package.
Increasing Salary
To increase your salary from a Mid-level Data Strategist position, consider the following strategies:
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Skill Enhancement: Continuously update your skills in emerging technologies and methodologies in AI/ML and data science. Specializing in high-demand areas like deep learning, natural language processing, or big data analytics can make you more valuable.
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Advanced Education: Pursuing further education, such as a master's degree or Ph.D. in data science, computer science, or a related field, can open up higher-paying opportunities.
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Leadership Roles: Transitioning into leadership or managerial roles can significantly increase your earning potential. This might involve leading a team of data scientists or strategists.
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Industry Shift: Moving to a higher-paying industry, such as finance or tech, can also result in a salary increase.
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Networking and Visibility: Building a strong professional network and increasing your visibility in the industry through speaking engagements, publications, or contributions to open-source projects can lead to better job offers.
Educational Requirements
Most mid-level data strategist positions require at least a bachelor's degree in a relevant field such as data science, computer science, statistics, mathematics, or engineering. However, many employers prefer candidates with a master's degree or higher, especially for roles that involve complex data analysis and strategic decision-making. A strong foundation in quantitative and analytical skills is essential, and coursework in machine learning, data mining, and statistical analysis is highly beneficial.
Helpful Certifications
While not always mandatory, certain certifications can enhance your qualifications and demonstrate your expertise to potential employers. Some valuable certifications include:
- Certified Analytics Professional (CAP)
- Google Professional Data Engineer
- Microsoft Certified: Azure Data Scientist Associate
- AWS Certified Machine Learning – Specialty
- SAS Certified Data Scientist
These certifications can validate your skills in data analysis, machine learning, and cloud-based data solutions, making you a more competitive candidate.
Required Experience
Typically, a mid-level data strategist role requires 3 to 5 years of experience in data analysis, data science, or a related field. Experience with data visualization tools, statistical software, and programming languages such as Python or R is often necessary. Additionally, experience in developing data-driven strategies and working cross-functionally with other departments is highly valued.
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