Salary for Mid-level / Intermediate Data Analyst in United States during 2024

Salary details

The average mid-level / intermediate Data Analyst salary lies between USD 75,000 and USD 126,500 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. 17, 2024

Salary trend

Top 20 Job Tags for Mid-level / Intermediate Data Analyst roles

The three most common job tag items assiciated with mid-level / intermediate Data Analyst job listings are SQL, Python and Data analysis. 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:

SQL | 1598 jobs Python | 1108 jobs Data analysis | 1105 jobs Statistics | 1039 jobs Excel | 975 jobs Tableau | 935 jobs Power BI | 834 jobs Engineering | 791 jobs Computer Science | 689 jobs Data visualization | 633 jobs Data Analytics | 604 jobs Research | 559 jobs Mathematics | 554 jobs R | 550 jobs Finance | 477 jobs Data quality | 414 jobs Testing | 380 jobs Business Intelligence | 374 jobs Economics | 359 jobs Data management | 333 jobs

Top 20 Job Perks/Benefits for Mid-level / Intermediate Data Analyst roles

The three most common job benefits and perks assiciated with mid-level / intermediate Data Analyst 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 | 1237 jobs Health care | 876 jobs Flex hours | 605 jobs Competitive pay | 509 jobs Startup environment | 424 jobs Equity / stock options | 415 jobs Insurance | 402 jobs Team events | 382 jobs Flex vacation | 368 jobs Salary bonus | 348 jobs Parental leave | 323 jobs Medical leave | 313 jobs Wellness | 259 jobs 401(k) matching | 243 jobs Home office stipend | 89 jobs Flexible spending account | 77 jobs Fitness / gym | 76 jobs Unlimited paid time off | 76 jobs Transparency | 75 jobs Gear | 71 jobs

Salary Composition

The salary for a Mid-level/Intermediate Data Analyst in the AI/ML/Data Science field typically consists of a base salary, performance bonuses, and sometimes additional remuneration such as stock options or profit-sharing. The base salary is the fixed component and usually makes up the majority of the total compensation package. Performance bonuses can vary significantly depending on the company's success and individual performance, often ranging from 5% to 20% of the base salary. Additional remuneration, like stock options, is more common in tech companies, especially startups, and can be a significant part of the compensation in high-growth regions like Silicon Valley.

Regional differences also play a role; for instance, salaries in tech hubs like San Francisco or New York City tend to be higher due to the cost of living and demand for skilled professionals. Industry can also impact salary composition, with finance and tech industries typically offering higher compensation packages compared to non-profit or government sectors. Company size is another factor; larger companies may offer more comprehensive benefits and bonuses, while smaller companies might offer more equity or stock options.

Steps to Increase Salary

To increase your salary from a Mid-level Data Analyst position, consider the following strategies:

• Skill Enhancement: Continuously upgrade your skills in advanced analytics, machine learning, and data visualization tools. Proficiency in programming languages like Python or R, and experience with big data technologies like Hadoop or Spark, can make you more valuable.

• Specialization: Focus on a niche area within data science, such as natural language processing, computer vision, or deep learning, which are in high demand and can command higher salaries.

• Advanced Education: Pursuing a master's degree or a Ph.D. in data science, computer science, or a related field can open up higher-paying opportunities.

• Networking and Professional Development: Attend industry conferences, join professional organizations, and engage in networking to learn about new opportunities and trends.

• Leadership and Management Skills: Developing skills in project management and leadership can prepare you for roles with more responsibility and higher pay, such as a Data Science Manager or Director.

Educational Requirements

Most Mid-level Data Analyst positions require at least a bachelor's degree in a relevant field such as data science, computer science, statistics, mathematics, or engineering. Some positions may prefer or require a master's degree, especially in competitive markets or for roles that involve more complex data analysis and modeling tasks.

Helpful Certifications

Certifications can enhance your resume and demonstrate your commitment to the field. Some valuable certifications include:

• Certified Analytics Professional (CAP): A widely recognized certification that covers the end-to-end analytics process.
• Microsoft Certified: Azure Data Scientist Associate: Useful if you work with Microsoft's Azure platform.
• Google Professional Data Engineer: Beneficial for those working with Google Cloud Platform.
• AWS Certified Data Analytics – Specialty: Ideal for professionals using Amazon Web Services.

Experience Requirements

Typically, a Mid-level Data Analyst is expected to have 2-5 years of experience in data analysis or a related field. This experience should include hands-on work with data analytics tools and technologies, as well as a proven track record of using data to drive business decisions. Experience in a specific industry can also be advantageous, as it provides context and understanding of industry-specific data challenges and opportunities.

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