Data Quality Lead Salary in United States during 2024
💰 The median Data Quality Lead Salary in United States during 2024 is USD 147,100
✏️ This salary info is based on 30 individual salaries reported during 2024
Salary details
The average Data Quality Lead salary lies between USD 113,840 and USD 170,760 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.
- Job title
- Data Quality Lead
- Experience
- all levels
- Region
- United States
- Salary year
- 2024
- Sample size
- 30
- 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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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:Top 20 Job Tags for Data Quality Lead roles
The three most common job tag items assiciated with Data Quality Lead job listings are Data quality, Data governance and Data management. 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:
Data quality | 58 jobs Data governance | 32 jobs Data management | 24 jobs Banking | 15 jobs Finance | 15 jobs Power BI | 15 jobs SQL | 14 jobs Data strategy | 9 jobs Research | 8 jobs Architecture | 8 jobs Privacy | 7 jobs Excel | 6 jobs Computer Science | 6 jobs Python | 5 jobs R | 5 jobs Machine Learning | 5 jobs Data Analytics | 5 jobs Testing | 5 jobs Agile | 5 jobs Data analysis | 5 jobsTop 20 Job Perks/Benefits for Data Quality Lead roles
The three most common job benefits and perks assiciated with Data Quality Lead job listings are Career development, Health care and Insurance. 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 | 34 jobs Health care | 33 jobs Insurance | 27 jobs Wellness | 25 jobs Medical leave | 23 jobs Competitive pay | 21 jobs Team events | 18 jobs Flex hours | 17 jobs Equity / stock options | 15 jobs Flex vacation | 11 jobs Conferences | 10 jobs Fertility benefits | 10 jobs 401(k) matching | 8 jobs Salary bonus | 5 jobs Parental leave | 4 jobs Fitness / gym | 2 jobs Startup environment | 2 jobs Transparency | 2 jobs Flat hierarchy | 1 jobs Relocation support | 1 jobsSalary Composition for a Data Quality Lead
The salary for a Data Quality Lead in the AI/ML/Data Science field 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 performance and individual achievements, often ranging from 10% to 20% of the base salary. Additional remuneration might include stock options, especially in tech companies or startups, and comprehensive benefits packages that cover health insurance, retirement plans, and other perks.
The composition of the salary can vary based on several factors:
- Region: Salaries in tech hubs like Silicon Valley, New York, or Seattle tend to be higher due to the cost of living and demand for skilled professionals.
- Industry: Industries such as finance, healthcare, and technology often offer higher salaries compared to non-profit or government sectors.
- Company Size: Larger companies may offer more competitive salaries and benefits, while startups might offer equity as part of the compensation package.
Steps to Increase Salary from a Data Quality Lead Position
To increase your salary from a Data Quality Lead position, consider the following strategies:
- Skill Enhancement: Continuously update your skills in data science, machine learning, and data quality management. Specializing in emerging technologies or methodologies can make you more valuable.
- Advanced Education: Pursuing advanced degrees such as a Master's or Ph.D. in Data Science, Computer Science, or a related field can open up higher-paying opportunities.
- Leadership Roles: Aim for leadership positions such as Data Quality Manager or Director of Data Quality, which typically offer higher salaries.
- Industry Transition: Consider moving to industries that pay higher salaries for data quality roles, such as finance or tech.
- Networking and Professional Development: Engage in networking opportunities and professional organizations to learn about higher-paying opportunities and industry trends.
Educational Requirements for a Data Quality Lead
Most Data Quality Lead positions require at least a bachelor's degree in a relevant field such as Computer Science, Data Science, Information Technology, or Statistics. However, many employers prefer candidates with a master's degree or higher, especially for senior roles. Advanced degrees provide a deeper understanding of data management, analytics, and quality assurance, which are crucial for this position.
Helpful Certifications for a Data Quality Lead
Certifications can enhance your credentials and demonstrate expertise in data quality and management. Some valuable certifications include:
- Certified Information Systems Auditor (CISA)
- Certified Data Management Professional (CDMP)
- Certified Information Management Professional (CIMP)
- Six Sigma Certification (for process improvement and quality management)
- AWS Certified Data Analytics (for cloud-based data solutions)
These certifications can provide a competitive edge and are often recognized by employers as a testament to your skills and commitment to the field.
Experience Required for a Data Quality Lead
Typically, a Data Quality Lead position requires several years of experience in data management, quality assurance, or a related field. Employers often look for candidates with 5-10 years of experience, including hands-on experience with data quality tools and methodologies. Experience in leading teams, managing projects, and implementing data quality frameworks is also highly valued.
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