Lead Data Quality Analyst information
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$28.61 - $34.09
6% of jobs
$34.09 - $39.58
7% of jobs
$43.04 is the 25th percentile. Wages below this are outliers.
$39.58 - $45.06
18% of jobs
$45.06 - $50.55
15% of jobs
The median wage is $51.61 / hr.
$50.55 - $56.03
19% of jobs
$56.03 - $61.52
9% of jobs
$61.61 is the 75th percentile. Wages above this are outliers.
$61.52 - $67
15% of jobs
$72.49 - $77.97
2% of jobs
$77.97 - $83.46
0% of jobs
$83.46 - $88.94
5% of jobs
How much do lead data quality analyst jobs pay per hour?
As of Sep 14, 2026, the average hourly pay for lead data quality analyst in the United States is $55.11, according to ZipRecruiter salary data. Most workers in this role earn between $43.03 and $62.98 per hour, depending on experience, location, and employer.
A Lead Data Quality Analyst oversees the processes that ensure data is accurate, consistent, and reliable across an organization. They develop and implement data quality standards, monitor data integrity, and lead a team of analysts in identifying and resolving data issues. Additionally, they collaborate with stakeholders to define data quality metrics and support data governance initiatives. Their work is essential for enabling accurate business insights and decision-making.
To thrive as a Lead Data Quality Analyst, you need a strong background in data management, statistical analysis, and a relevant degree in computer science, information systems, or a related field. Experience with data quality tools (such as Informatica, Talend, or IBM InfoSphere), SQL, and sometimes certifications like CDMP are typically required. Exceptional analytical thinking, attention to detail, and strong leadership and communication skills help in managing teams and collaborating with stakeholders. These skills and qualifications are essential to ensure the integrity, accuracy, and consistency of data, which underpin effective decision-making and operational efficiency.
A Lead Data Quality Analyst regularly works with teams such as IT, business intelligence, and operations to identify data quality issues and implement solutions. They often facilitate cross-functional meetings to align data standards and work closely with data owners to resolve discrepancies. Strong communication and project management skills are essential, as the role requires translating complex data findings into actionable insights for non-technical stakeholders. Effective collaboration helps ensure that data-driven decisions are based on accurate and reliable information across the organization.
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