Data Quality Lead

Johnson Controls•Glendale, AZ

About The Position

The Data Quality Lead defines, implements, and governs enterprise data quality practices to ensure data is trusted, accurate, complete, timely, and consistent across the organization. The role partners with business stakeholders, Data Governance teams, data engineers, architects, analysts, and application owners to establish standards, monitor data health, resolve issues, and improve data reliability. As the primary authority for enterprise data quality strategy, metrics, controls, and continuous improvement, the role strengthens data foundations for business operations, reporting, analytics, AI/ML, regulatory compliance, and digital transformation. The position develops enterprise frameworks, policies, standards, and operating models; establishes data quality ownership and accountability; and defines rules, thresholds, SLOs, SLAs, and support procedures. It leads monitoring, incident management, assessments, remediation, root cause analysis, KPI development, and executive reporting. The role also supports regulatory and audit compliance, metadata management, data lineage, data observability, and implementation of enterprise data quality solutions across Snowflake, Azure, and data integration platforms, including through automation and AI-driven capabilities.

Requirements

Bachelor's degree in Information Systems, Computer Science, Data Management, Engineering, or a related field.
8+ years of experience in data management, data governance, analytics, or data engineering.
3+ years of experience leading enterprise data quality programs.
Experience leading cross-functional initiatives within complex organizations.
Demonstrated experience establishing enterprise data quality frameworks and controls.
Experience with cloud platforms and technologies including Snowflake, Databricks, Azure, and AWS.
Experience with data governance capabilities, including data catalogs, metadata management, business glossaries, and data lineage.
Experience with SQL and data analysis tools.
Experience with data integration and ETL platforms.
Leadership competencies: strategic thinking, influencing without authority, stakeholder management, data governance expertise, root cause analysis, continuous improvement, process optimization, change management, executive communication, and team development.

Nice To Haves

Master's degree.
Experience with KPMG, Deloitte, PwC, or E&Y.
Certified Data Management Professional (CDMP) certification.
Collibra Certification.
Informatica Certification.
Microsoft Purview Certification.
Knowledge of DAMA-DMBOK best practices.
Experience supporting AI/ML and advanced analytics initiatives.
Experience with data observability and DataOps practices.

Responsibilities

Develop and execute an enterprise data quality strategy aligned with business objectives.
Establish data quality frameworks, policies, standards, principles, operating models, and best practices.
Promote a strong data quality culture and lead cross-functional initiatives to improve data trust and usability.
Partner with Data Governance teams and business data stewards to identify and define critical data elements.
Establish clear data quality ownership and accountability models.
Define data quality rules, thresholds, service-level objectives, service-level agreements, and operational support procedures.
Facilitate governance forums and data quality review boards.
Support compliance with applicable regulatory and audit requirements.
Design and implement proactive data quality monitoring frameworks and controls for completeness, accuracy, consistency, timeliness, validity, uniqueness, and integrity.
Oversee implementation of automated data quality checks within data pipelines.
Monitor data quality dashboards and scorecards to identify trends, risks, and improvement opportunities.
Drive incident management and root cause analysis for data quality issues.
Lead enterprise data quality assessments and remediation activities.
Prioritize data quality defects based on business impact and coordinate corrective actions with engineering teams.
Develop enterprise data quality KPIs, scorecards, and executive reporting.
Communicate data quality health, risks, and opportunities to leadership and key stakeholders.
Partner with Business Data Owners, Data Stewards, Data Governance teams, Enterprise Architects, Data Engineers, Application Owners, Analytics teams, and Compliance and Audit teams.
Serve as a trusted advisor on data quality and influence organizational adoption of quality standards.
Evaluate and support implementation of enterprise data quality solutions.
Define data quality monitoring across Snowflake, Azure, and data integration platforms.
Support metadata management, data lineage, and observability initiatives.
Use automation and AI-driven capabilities to strengthen data quality monitoring and continuous improvement.