Salary for Senior-level / Expert Data Quality Lead in United States during 2024

Salary details

The average senior-level / expert 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.

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: Dec. 22, 2024

Top 20 Job Tags for Senior-level / Expert Data Quality Lead roles

The three most common job tag items assiciated with senior-level / expert 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 | 56 jobs Data governance | 32 jobs Data management | 22 jobs Banking | 15 jobs Finance | 15 jobs Power BI | 15 jobs SQL | 14 jobs Architecture | 8 jobs Data strategy | 7 jobs Privacy | 7 jobs Research | 6 jobs Excel | 6 jobs Python | 5 jobs Machine Learning | 5 jobs Testing | 5 jobs Agile | 5 jobs Data analysis | 5 jobs Engineering | 4 jobs Consulting | 4 jobs Security | 4 jobs

Top 20 Job Perks/Benefits for Senior-level / Expert Data Quality Lead roles

The three most common job benefits and perks assiciated with senior-level / expert 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 jobs

Salary Composition

In the United States, the salary composition for a Senior-level or Expert Data Quality Lead typically includes a combination of a fixed base salary, performance-based bonuses, and additional remuneration such as stock options or equity, especially in tech companies. The base salary often constitutes the majority of the total compensation package, ranging from 70% to 85%. Bonuses can vary significantly depending on the company's performance and individual achievements, usually accounting for 10% to 20% of the total compensation. Additional remuneration, such as stock options, is more common in larger tech companies or startups and can make up 5% to 15% of the total package. Regional differences also play a role; for instance, salaries in tech hubs like San Francisco or New York City tend to be higher due to the cost of living and competitive job markets. Industry-wise, tech and finance sectors often offer higher compensation compared to healthcare or manufacturing.

Next Steps to Increase Salary

To increase your salary further from this position, consider the following strategies:

• Skill Enhancement: Continuously update your skills in emerging technologies and methodologies in AI/ML and data science. Specializing in niche areas like deep learning, natural language processing, or big data analytics can make you more valuable.
• Leadership Roles: Aim for roles with greater responsibility, such as a Director of Data Quality or Chief Data Officer, which typically come with higher compensation.
• Networking: Build a strong professional network by attending industry conferences, joining relevant professional groups, and engaging in online forums. Networking can open up opportunities for higher-paying positions.
• Negotiation: When offered a new position or during performance reviews, negotiate for higher pay by highlighting your achievements and the value you bring to the organization.
• Consulting: Consider offering your expertise as a consultant, which can provide additional income streams and potentially lead to lucrative opportunities.

Educational Requirements

For a Senior-level or Expert Data Quality Lead position, a bachelor's degree in a relevant field such as Computer Science, Data Science, Statistics, or Information Technology is typically required. However, many employers prefer candidates with a master's degree or even a Ph.D. in these fields, as advanced education often correlates with a deeper understanding of complex data systems and analytical techniques. Additionally, coursework in business administration or management can be beneficial for those looking to move into leadership roles.

Helpful Certifications

While not always mandatory, certain certifications can enhance your qualifications and demonstrate your commitment to the field. Some valuable certifications include:

• Certified Data Management Professional (CDMP): Offered by DAMA International, this certification validates your expertise in data management.
• Certified Information Systems Auditor (CISA): Useful for those focusing on data quality in the context of information systems.
• AWS Certified Big Data – Specialty: Demonstrates your ability to design and implement big data solutions on the AWS platform.
• Google Professional Data Engineer: Validates your ability to design, build, and operationalize data processing systems.

Required Experience

Typically, a Senior-level or Expert Data Quality Lead position requires at least 7 to 10 years of experience in data management, data quality, or related fields. This experience should include a proven track record of leading data quality initiatives, managing teams, and implementing data governance frameworks. Experience in specific industries, such as finance or healthcare, can also be advantageous, as it provides domain-specific knowledge that can be critical for certain roles.

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