Salary for Mid-level / Intermediate Data Analyst in United States during 2023

Salary details

The average mid-level / intermediate Data Analyst salary lies between USD 75,000 and USD 115,950 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 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 2023 and the number of open jobs that where associated with them during that period:

SQL | 662 jobs Python | 453 jobs Data analysis | 445 jobs Tableau | 409 jobs Statistics | 406 jobs Power BI | 337 jobs Excel | 335 jobs Engineering | 323 jobs Computer Science | 265 jobs Mathematics | 254 jobs Data Analytics | 226 jobs Research | 223 jobs Data visualization | 215 jobs R | 204 jobs Finance | 180 jobs Business Intelligence | 161 jobs Data quality | 151 jobs Machine Learning | 150 jobs Testing | 149 jobs Economics | 139 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 2023 and the number of open jobs that where offering them during that period:

Career development | 471 jobs Health care | 369 jobs Flex hours | 260 jobs Startup environment | 250 jobs Competitive pay | 230 jobs Team events | 183 jobs Flex vacation | 173 jobs Insurance | 153 jobs Parental leave | 134 jobs Salary bonus | 126 jobs Equity / stock options | 104 jobs Medical leave | 98 jobs 401(k) matching | 94 jobs Gear | 64 jobs Wellness | 57 jobs Fitness / gym | 50 jobs Home office stipend | 49 jobs Unlimited paid time off | 37 jobs Relocation support | 19 jobs Yoga | 17 jobs

Salary Composition for Mid-level Data Analysts

The salary for a mid-level data analyst in the United States 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 policy and the individual’s performance, often ranging from 5% to 15% of the base salary. Additional remuneration might include stock options, especially in tech companies, or other benefits like health insurance, retirement plans, and paid time off.

Regional differences can also impact salary composition. For instance, data analysts in tech hubs like San Francisco or New York might receive higher base salaries and stock options due to the higher cost of living and competitive job market. Industry also plays a role; data analysts in finance or healthcare might see different bonus structures compared to those in retail or manufacturing. Company size can influence the availability of stock options and the scale of bonuses, with larger companies often offering more comprehensive packages.

Steps to Increase Salary

To increase your salary from a mid-level data analyst position, consider the following strategies:

• Skill Enhancement: Acquire advanced skills in machine learning, data engineering, or specialized software tools. Proficiency in programming languages like Python or R, and expertise in data visualization tools like Tableau or Power BI, can make you more valuable.

• Advanced Education: Pursuing a master's degree in data science, statistics, or a related field can open up higher-paying opportunities and leadership roles.

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

• Certifications: Obtain relevant certifications that can validate your skills and knowledge, making you a more attractive candidate for higher-paying roles.

• Performance and Negotiation: Consistently exceed performance expectations and be prepared to negotiate your salary during performance reviews or when offered a new position.

Educational Requirements

Most mid-level data analyst positions require at least a bachelor’s degree in a related field such as data science, statistics, computer science, or mathematics. Some employers may prefer candidates with a master’s degree, especially for roles that involve more complex data analysis or leadership responsibilities. A strong educational background provides the foundational knowledge necessary for data analysis, including statistical methods, data management, and programming.

Helpful Certifications

Certifications can enhance your credentials and demonstrate your commitment to the field. Some valuable certifications for data analysts include:

• Certified Analytics Professional (CAP): This certification covers the entire analytics process, from framing business problems to deploying models.

• Microsoft Certified: Data Analyst Associate: Focuses on using Power BI to maximize the value of data assets.

• Google Data Analytics Professional Certificate: Offers a comprehensive introduction to data analytics, including data cleaning, analysis, and visualization.

• SAS Certified Data Scientist: Validates your ability to manipulate and gain insights from big data using SAS and open-source tools.

Experience Requirements

Typically, a mid-level data analyst position requires 2 to 5 years of experience in data analysis or a related field. This experience should include hands-on work with data collection, cleaning, analysis, and visualization. Experience with specific tools and technologies relevant to the industry or company can also be crucial. Demonstrating a track record of successful projects and the ability to derive actionable insights from data is often essential.

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