Enterprise Data Quality Lead

This is remote

Enterprise Data Quality Lead

Market rate

• Must have at least 5 years of experience in Data Quality Management specifically (must be apparent in the resume, not just Data Governance)
• The data quality experience must be in CPG, or Manufacturing, or Life Science (an industry that deals with physical goods)
• Must be senior enough for the role (~15+ years of total professional experience or a little less if the candidate is really good)
• Can lead Data Quality at an enterprise level for a large company, $5B+ revenue
• Can set up a cohesive data quality framework from end to end, and can comfortably get into details

Responsibilities :

Data quality strategy and maturity growth

Define and drive the enterprise data quality strategy and roadmap, ensuring it is embedded in the organization's

overall data strategy and transformation agenda

Drive enterprise-wide maturity growth, shifting from reactive cleanup to proactive, prevention-first practices

Oversee the design, rollout, and ongoing enhancement of an enterprise data quality dashboard and automated

reporting, ensuring visibility of quality KPIs across all domains for all stakeholders

Enterprise quality standards & rule governance

Establish and enforce enterprise-wide data quality standards aligned with governance policies, business

requirements, and regulatory obligations

Govern the end-to-end lifecycle of data quality rules - from intake and design to approval, deployment, and

retirement - ensuring traceability to business requirements and clear ownership

Approve critical rule definitions and thresholds, ensuring accountability and remediation paths are in place

Integration of data quality into engineering and release processes

Ensure data quality controls are embedded by design into data product development and release processes,

partnering with IT Delivery and Architecture teams to institutionalize quality gates

Monitoring, issue triage, root-cause analysis, and remediation

Own the enterprise data quality monitoring and incident management process, ensuring effective triage,

escalation, root cause analysis, resolution and communication

Validate that corrective and preventive measures are sustainable and embedded into business and technical

processes

Master and reference data management, matching, and survivorship (with MDM team)

Define and govern standardization, matching, de-duplication, and survivorship logic for master and reference data

domains in partnership with MDM teams

Enforce harmonization of reference data across systems, and partner with Data Stewards / Data Domain Leads to

ensure upstream governance and prevention of duplication or conflicts

Skill Set :

Data quality frameworks and tooling expertise : Hands-on

experience with profiling, rule authoring, monitoring, and data

observability platforms; ability to standardize methods across

diverse technologies

Business acumen : Skill in translating business logic into

executable checks with measurable thresholds

Engineering and CI / CD literacy : Knowledge of data pipeline

orchestration, version control, automated testing, and

Continuous Integration / Continuous Delivery so quality gates are

enforced consistently

Master Data Management knowledge : Understanding of

standardization, matching, de-duplication, and survivorship and

how these controls integrate with upstream and downstream

systems

Stakeholder influence and facilitation : Ability to align Data

Governance lead, Data Stewards / Data Domain Leads, Product

Managers, and Delivery teams on thresholds, trade-offs, and

remediation plans