Senior Data Quality Manager

Eaton's Corporate Sector division is currently seeking a Senior Data Quality Manager. This position has a preference for an individual working on a hybrid scheudle from one of our IT Corporate sites including Beachwood OH, Galesburg MI, Houston TX, Menomonee Falls WI, Moon Township PA or Raleigh NC other Eaton sites may be considered for hybrid work.

The expected annual salary range for this role is $141000.03 - $206800.04 a year. This position is also eligible for a variable incentive program.

Please note the salary information shown above is a general guideline only. Salaries are based upon candidate skills, experience, and qualifications, as well as market and business considerations.

What you'll do:

Job Responsibilities:
Enterprise scorecard & operating rhythm
• Own the Enterprise Data Health Metrics, including the Enterprise Data Quality Scorecard (connected metrics to underlying models/reports), ensuring consistent definitions, targets, and drill-downs to remediation owners.
• Chair the bi-weekly Data Quality Operating Review with PMO: highlight top issues and risks, track MTTR, drive RCAs and prevention actions, and publish decisions/actions.
Rules, standards, and prevention
• Design and govern the DQ rule lifecycle (critical data elements, dimensions, thresholds, exception handling), with shift-left prevention in bronze→silver→gold flows (medallion).
• Establish data contracts and test gates in CI/CD so bad data is caught pre-production; scale rule reuse via libraries and templates.
AI-era data quality & observability
• Introduce AI-assisted DQ: anomaly/outlier detection, drift monitoring, LLM-assisted rule authoring/explanations, and synthetic data for validation.
• Partner with AI & Analytics Governance to ensure training/evaluation datasets meet quality, lineage, and consent constraints; contribute to trustworthy AI practices.
Cross-functional leadership
• Partner with PMO & Governance Ops (Council, enablement, adoption), Domain Stewards in functions, MDM team, and AI & Analytics teams to prioritize fixes with clear owners and dates.
• Work with Data Engineering to embed medallion/lakehouse guardrails (validation at each hop, schema evolution patterns, replay/rollback).
Value, risk, and communications
• Quantify business impact of fixes (defect burn-down, SLA attainment, incident cost avoidance); report outcomes to the D&A Council.
• Maintain an executive-ready DQ narrative: where trust is improving, where risk remains, and which guardrails will accelerate value next.

Qualifications:

Required (Basic) Qualifications:
• Bachelors degree from an accredited institution required.
• Minimum 10 years in data management/analytics with 5 years in enterprise data governance and or quality (rules, scorecards, issue management) in federated organizations.
• Must be able to work in the United States without corporate sponsorship now and within the future.

Skills:

Position Criteria:
• Proven product ownership of dashboards/scorecards (backlog, UX, adoption, measurable outcomes).
• Hands-on with root-cause analysis, defect prevention, and driving cross-domain remediation to closure.
• Leads a lean operating model for data quality, including a standardized action-plan process (clear owners, due dates, countermeasures), tiered operating reviews, and visual management to track and sustain improvements.
• KPI / Metric Layer & Dashboards: semantic model understanding + Metrics/Scorecards design, KPI definition & standardization (num/denom, filters, thresholds), connected metrics, scorecard roll-ups, drill paths, metric certification/versioning
• Alation (or similar): glossary curation, certification workflows, stewardship enablement, catalog adoption analytics.
• Lakehouse / medallion on Azure Databricks/Delta Lake: bronze/silver/gold validation patterns; data contracts; CI/CD for pipelines.