Salary for Mid-level / Intermediate Data Analyst during 2024

Salary details

The average mid-level / intermediate Data Analyst salary lies between USD 70,000 and USD 125,000 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: 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 | 1592 jobs Python | 1103 jobs Data analysis | 1099 jobs Statistics | 1035 jobs Excel | 972 jobs Tableau | 933 jobs Power BI | 833 jobs Engineering | 786 jobs Computer Science | 686 jobs Data visualization | 631 jobs Data Analytics | 603 jobs Research | 558 jobs Mathematics | 552 jobs R | 549 jobs Finance | 475 jobs Data quality | 413 jobs Testing | 380 jobs Business Intelligence | 372 jobs Economics | 358 jobs KPIs | 331 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 | 1232 jobs Health care | 871 jobs Flex hours | 602 jobs Competitive pay | 507 jobs Startup environment | 424 jobs Equity / stock options | 415 jobs Insurance | 400 jobs Team events | 380 jobs Flex vacation | 367 jobs Salary bonus | 346 jobs Parental leave | 321 jobs Medical leave | 312 jobs Wellness | 258 jobs 401(k) matching | 242 jobs Home office stipend | 89 jobs Fitness / gym | 76 jobs Flexible spending account | 76 jobs Unlimited paid time off | 76 jobs Transparency | 75 jobs Gear | 71 jobs

Salary Composition

The salary composition for a Mid-level/Intermediate Data Analyst in AI/ML/Data Science can vary significantly based on factors such as region, industry, and company size. Generally, the salary is composed of a fixed base salary, which forms the bulk of the compensation package. This base salary can range from 70% to 90% of the total compensation. In addition to the base salary, many companies offer performance-based bonuses, which can account for 10% to 20% of the total compensation. These bonuses are often tied to individual performance metrics, team achievements, or company-wide goals.

Additional remuneration may include stock options or equity, especially in tech startups or larger tech companies, which can be a significant part of the compensation package. Benefits such as health insurance, retirement contributions, and other perks like remote work stipends or professional development funds can also add value to the overall compensation. In regions with a high cost of living, such as the San Francisco Bay Area or New York City, salaries tend to be higher to offset living expenses.

Increasing Salary

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

• Skill Enhancement: Continuously upgrade your technical skills, especially in high-demand areas like machine learning, deep learning, and big data technologies. Proficiency in programming languages such as Python, R, and SQL is crucial, but adding skills in cloud computing (AWS, Azure, Google Cloud) can make you more valuable.

• Advanced Education: Pursuing a master's degree or specialized certifications in data science or related fields can make you more competitive for higher-paying roles.

• Networking and Professional Development: Attend industry conferences, workshops, and webinars to expand your professional network. Engaging with industry leaders and peers can open up new opportunities and provide insights into salary trends.

• Leadership and Management Skills: Developing soft skills such as leadership, project management, and communication can position you for roles with more responsibility and higher pay.

• Explore Different Industries: Some industries, like finance and healthcare, may offer higher salaries for data analysts due to the complexity and critical nature of the data they handle.

Educational Requirements

Most Mid-level/Intermediate Data Analyst positions require at least a bachelor's degree in a related field such as computer science, statistics, mathematics, or engineering. However, a master's degree in data science, analytics, or a related discipline can be advantageous and is often preferred by employers. The educational background should provide a strong foundation in statistical analysis, data manipulation, and programming.

Helpful Certifications

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

• Certified Analytics Professional (CAP): This certification is recognized across industries and validates your ability to transform data into valuable insights.

• Microsoft Certified: Azure Data Scientist Associate: This certification is beneficial if you work with Microsoft's Azure platform.

• Google Professional Data Engineer: This certification is ideal for those working with Google Cloud technologies.

• AWS Certified Data Analytics – Specialty: This certification is useful for professionals working with Amazon Web Services.

• SAS Certified Data Scientist: This certification is valuable for those using SAS tools and technologies.

Required Experience

Typically, a Mid-level/Intermediate Data Analyst role requires 2 to 5 years of relevant experience. This experience should include hands-on work with data analysis, statistical modeling, and data visualization tools. Experience in specific industries or with particular types of data can also be beneficial, depending on the job requirements.

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