Key Responsibilities and Required Skills for Data Quality Manager

🎯 Role Definition

The Data Quality Manager owns the definition, implementation and continuous improvement of the enterprise data quality program. This role leads cross-functional teams and data stewards to establish data quality standards, policies, monitoring and remediation processes across operational and analytical systems. The Data Quality Manager translates business requirements into automated rules and tests, drives root-cause resolution, and reports KPIs that increase data trust, reduce operational risk, and accelerate data-driven decision making.

📈 Career Progression

Typical Career Path

Entry Point From:

Data Quality Analyst / Data Steward
Data Analyst or Business Intelligence Analyst
Data Engineer or ETL Developer

Advancement To:

Senior Manager / Head of Data Quality
Director of Data Governance or Master Data Management (MDM)
VP / Chief Data Officer (CDO) or Head of Data Platforms

Lateral Moves:

Data Governance Manager
Master Data Management (MDM) Lead
Analytics Engineering Manager

Core Responsibilities

Primary Functions

Design, build and own an enterprise data quality framework including definitions, scorecards, KPIs, SLAs and automated monitoring that cover master, reference, transactional and analytical datasets.
Lead the implementation and operationalization of data quality rules and tests in pipelines using tools such as Great Expectations, Informatica Data Quality, Talend, Ataccama or custom SQL/Python tests to ensure early detection and prevention of data issues.
Create and maintain data quality scorecards and dashboards (Tableau, Power BI, Looker) to communicate data health, trends and business impact to senior stakeholders and product owners.
Establish and manage a data stewardship program: recruit, train and coordinate data stewards and subject matter experts to own data definitions, remediation workflows and rule approvals.
Partner with data engineering and platform teams to integrate data quality checks into ETL/ELT pipelines and CI/CD processes, ensuring tests run in development, staging and production environments.
Perform systematic data profiling, metadata analysis and lineage mapping to identify root causes of data defects and prioritize remediation actions with application owners.
Define and enforce data contracts and acceptance criteria between source system owners and data consumers to reduce downstream rework and improve SLAs for data delivery.
Lead cross-functional incident response for high-impact data quality events, run RCA (root cause analysis), and drive permanent fixes while reporting remediation status and business impact.
Design and maintain master data management (MDM) processes and validation rules to improve golden record accuracy and consistency across domains (customer, product, financial).
Translate complex business requirements into measurable data quality rules and maintain a prioritized backlog of data quality engineering tasks.
Lead vendor selection and manage relationships with third-party data quality, catalog and governance tool providers; negotiate licensing and service-level agreements.
Define and measure ROI and business outcomes from data quality initiatives, including operational cost savings, compliance risk reduction and improvements in analytics accuracy.
Drive data privacy, security and regulatory compliance (GDPR, CCPA, HIPAA where applicable) by ensuring data accuracy, retention and masking rules are implemented and validated.
Build and run automated alerting, remediation playbooks, and workflow orchestration for recurring data issues using orchestration tools and ticketing systems (Jira, ServiceNow).
Mentor and manage a team of data quality engineers, analysts and stewards, including hiring, performance management, career development and resource planning.
Collaborate with product, finance, operations and legal stakeholders to translate business impact into priority remediation work and to define acceptable data quality levels for critical KPIs.
Implement data lineage and impact analysis to understand upstream sources of defects before changes are made to production data models.
Establish best practices for metadata management and data catalog integration to improve discoverability, trust and reuse of data assets.
Run periodic data quality audits and maturity assessments to identify gaps in governance, tooling and organizational readiness, and build multi-year roadmaps to close gaps.
Develop and deliver training materials, playbooks and workshops for data producers and consumers focused on prevention, detection and remediation of data quality issues.
Maintain a forward-looking strategy for automation of repetitive quality tasks (auto-correction, rule tuning) leveraging machine learning where appropriate while ensuring traceability and approval.
Partner with cloud platform and security teams to ensure data quality processes are scalable and secure across AWS, Azure or Google Cloud environments.

Secondary Functions

Support ad-hoc data requests and exploratory data analysis.
Contribute to the organization's data strategy and roadmap.
Collaborate with business units to translate data needs into engineering requirements.
Participate in sprint planning and agile ceremonies within the data engineering team.
Provide subject matter expertise during new system onboarding to prevent data model and integration issues.
Assist with data catalog population and curate metadata annotations to improve business-friendly definitions and lineage.
Participate in vendor evaluations and proof-of-concepts for emerging data quality tools and automation frameworks.
Represent data quality priorities in governance boards and center-of-excellence forums.

Required Skills & Competencies

Hard Skills (Technical)

Strong SQL proficiency for profiling, root-cause analysis and implementing data quality queries across relational and columnar stores.
Experience writing data tests and assertions in Python (pandas, Great Expectations) or equivalent scripting languages.
Hands-on experience with enterprise data quality and governance tools such as Informatica Data Quality, Talend, Ataccama, Collibra, Alation or Great Expectations.
Knowledge of data cataloging, metadata management and lineage tools and practices.
Familiarity with ETL/ELT frameworks and orchestration tools (Airflow, dbt, NiFi) to embed quality checks in pipelines.
Experience with cloud data platforms (AWS, Azure, GCP) and cloud-native data services (Redshift, Snowflake, BigQuery).
Understanding of Master Data Management (MDM) concepts, de-duplication, matching, survivorship and hierarchies.
Experience with BI and visualization tools (Tableau, Power BI, Looker) to produce data quality dashboards and scorecards.
Familiarity with regulatory and privacy requirements (GDPR, CCPA, HIPAA) and techniques for data masking, pseudonymization and secure handling.
Strong grasp of data modeling, schema design and best practices for transactional and analytical systems.
Experience implementing automated CI/CD testing for data pipelines and data contract validation.
Comfortable with ticketing and issue-tracking systems (Jira, ServiceNow) and building remediation workflows.

Soft Skills

Excellent stakeholder management and ability to influence across engineering, product and business teams.
Clear and persuasive communication skills, able to translate technical data quality issues into business impact and remediation plans.
Strategic thinker with the capability to prioritize based on risk, value and regulatory requirements.
Strong leadership and people management, including mentoring and developing data teams.
Problem-solving mindset and attention to detail with an analytical, metrics-driven approach.
Project management skills and experience running cross-functional programs and initiatives.
Change management aptitude to drive adoption of new processes and tooling.
Ability to work in agile, iterative delivery environments and manage competing priorities.

Education & Experience

Educational Background

Minimum Education:

Bachelor's degree in Computer Science, Information Systems, Statistics, Mathematics, Data Science, Engineering, Business Analytics or a related field.

Preferred Education:

Master's degree in Data Science, Business Analytics, Information Systems, Computer Science, or an MBA with analytics focus.
Professional certifications such as CDMP (Certified Data Management Professional), TOGAF, or vendor certifications (Collibra, Informatica) are a plus.

Relevant Fields of Study:

Computer Science / Software Engineering
Information Systems / Management Information Systems
Data Science / Statistics / Applied Mathematics
Business Analytics / Finance / Economics

Experience Requirements

Typical Experience Range:

5–10+ years in data, analytics, or data engineering roles with at least 2–4 years specifically focused on data quality, governance or stewardship.

Preferred:

7+ years of progressive experience including hands-on implementation of data quality programs, leading teams of analysts/engineers, and delivering measurable improvements in data trust and business outcomes.
Demonstrated experience working in large-scale, multi-domain environments (CRM, ERP, Finance, Product) and with cloud data platforms and modern data stack technologies.