Data Monkey information
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$7.93 - $13.94
14% of jobs
$15.70 is the 25th percentile. Wages below this are outliers.
$13.94 - $19.95
38% of jobs
$19.95 - $25.96
15% of jobs
$25.96 - $31.97
7% of jobs
$32.47 is the 75th percentile. Wages above this are outliers.
$31.97 - $37.98
6% of jobs
$37.98 - $43.99
3% of jobs
$56.01 - $62.02
2% of jobs
$62.02 - $68.03
2% of jobs
$68.03 - $74.04
1% of jobs
How much do data monkey jobs pay per hour?
As of Aug 24, 2026, the average hourly pay for data monkey in the United States is $29.39, according to ZipRecruiter salary data. Most workers in this role earn between $15.62 and $37.50 per hour, depending on experience, location, and employer.
A Data Monkey is an informal term for someone who primarily handles data preparation, cleaning, and basic analysis tasks, often using tools like Excel, SQL, or data visualization software. They typically focus on collecting, transforming, and organizing large datasets to support business decisions or more advanced analytics. While the role can be a stepping stone to more technical data positions, it often involves repetitive or manual data processing tasks. The term is sometimes used humorously or self-deprecatingly in data-related fields.
To thrive as a Data Analyst, you need strong analytical skills, proficiency in statistics, and a relevant degree in mathematics, computer science, or a related field. Familiarity with data visualization tools (like Tableau or Power BI), SQL databases, and programming languages such as Python or R is typically required. Excellent attention to detail, problem-solving abilities, and effective communication skills help analysts translate data insights into actionable business recommendations. These competencies are crucial for extracting meaningful information from complex datasets and supporting data-driven decision-making.
Data Monkeys often encounter challenges such as dealing with inconsistent or incomplete data, managing large volumes of information efficiently, and ensuring data accuracy during cleaning and processing. They may also need to learn new tools or programming languages quickly to adapt to project needs. Collaborating with analysts and data scientists is essential to clarify requirements and ensure that data preparation aligns with team goals, making strong communication skills invaluable.
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