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Data Quality Manager

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April 11, 2024 Updated April 16, 2025 17 minute read

Data Quality Manager: A Comprehensive Career Guide

A Data Quality Manager (DQM) plays a crucial role in today's data-driven world. They are the guardians of an organization's data assets, ensuring that information is accurate, consistent, complete, and reliable. Think of them as the quality control experts for the vast streams of data flowing through a company, ensuring it meets high standards before being used for decision-making, analytics, or operations.

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Salaries for Data Quality Manager

City
Median
New York
$159,000
San Francisco
$152,000
Seattle
$152,000
See all salaries
City
Median
New York
$159,000
San Francisco
$152,000
Seattle
$152,000
Austin
$134,000
Toronto
$135,000
London
£74,000
Paris
€65,000
Berlin
€96,000
Tel Aviv
₪372,000
Singapore
S$110,000
Beijing
¥150,000
Shanghai
¥180,000
Shenzhen
¥589,000
Bengalaru
₹543,000
Delhi
₹850,000
Bars indicate relevance. All salaries presented are estimates. Completion of this course does not guarantee or imply job placement or career outcomes.

Reading list

We've selected some books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Data Quality Manager.
Provides a comprehensive overview of data quality, covering the key concepts, methodologies, and best practices. It valuable resource for anyone looking to improve the quality of their data.
Provides a comprehensive overview of data quality, covering the key concepts, methodologies, and best practices. It valuable resource for anyone looking to improve the quality of their data.
Provides a comprehensive overview of data quality, covering the key concepts, methodologies, and best practices. It valuable resource for anyone looking to improve the quality of their data.
Provides practical techniques for evaluating, improving, and using data, covering data quality assessment, data quality improvement, and data governance.
Provides a practical guide to data quality improvement. It covers the different techniques that can be used to improve data quality, and it provides advice on how to implement a data quality improvement program.
Provides an introduction to data quality in the real world. It covers the key concepts, methodologies, and best practices.
Provides a practical guide to data quality assurance, covering data quality assessment, data quality improvement, and data governance.
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