Associate Director, Data Quality Lead
Novartis
September 03, 2026
Remote friendly (East Hanover, NJ)
United States
IT
Key Responsibilities:
- Define and advance enterprise data quality standards, frameworks, policies, and measurement practices.
- Establish scalable quality rules, thresholds, scorecards, dashboards, and reusable control frameworks.
- Partner with stakeholders to translate business needs into measurable data quality requirements.
- Embed data quality controls throughout the data product lifecycle and certification process.
- Lead enterprise monitoring, reporting, and performance tracking for critical data assets.
- Drive data quality issue triage, root cause analysis, escalation, and resolution.
- Collaborate with engineering teams to automate quality checks and data observability.
- Support AI-ready data initiatives by ensuring trusted, governed, and high-quality datasets.
- Maintain documentation, remediation plans, operational trackers, and control evidence.
- Foster a culture of accountability, continuous improvement, and confidence in enterprise data.
Essential Requirements:
- 8+ years in data quality, including 3+ years leading teams in a regulated industry (pharma/life sciences strongly preferred).
- Strong understanding of data quality dimensions, data profiling, validation, cleansing, root cause analysis, and remediation.
- Knowledge of data governance frameworks, data stewardship, critical data elements, metadata, lineage, and data product certification.
- Familiarity with data quality tools, data observability platforms, SQL, data catalogs, workflow tools, and reporting dashboards.
- Understanding of data engineering concepts (pipelines, ingestion, transformation, controls, monitoring, production support).
- Ability to define measurable data quality rules and translate business needs into technical requirements.
- Knowledge of AI-ready data concepts (trusted datasets, lineage, quality thresholds, governed usage).
- Define and advance enterprise data quality standards, frameworks, policies, and measurement practices.
- Establish scalable quality rules, thresholds, scorecards, dashboards, and reusable control frameworks.
- Partner with stakeholders to translate business needs into measurable data quality requirements.
- Embed data quality controls throughout the data product lifecycle and certification process.
- Lead enterprise monitoring, reporting, and performance tracking for critical data assets.
- Drive data quality issue triage, root cause analysis, escalation, and resolution.
- Collaborate with engineering teams to automate quality checks and data observability.
- Support AI-ready data initiatives by ensuring trusted, governed, and high-quality datasets.
- Maintain documentation, remediation plans, operational trackers, and control evidence.
- Foster a culture of accountability, continuous improvement, and confidence in enterprise data.
Essential Requirements:
- 8+ years in data quality, including 3+ years leading teams in a regulated industry (pharma/life sciences strongly preferred).
- Strong understanding of data quality dimensions, data profiling, validation, cleansing, root cause analysis, and remediation.
- Knowledge of data governance frameworks, data stewardship, critical data elements, metadata, lineage, and data product certification.
- Familiarity with data quality tools, data observability platforms, SQL, data catalogs, workflow tools, and reporting dashboards.
- Understanding of data engineering concepts (pipelines, ingestion, transformation, controls, monitoring, production support).
- Ability to define measurable data quality rules and translate business needs into technical requirements.
- Knowledge of AI-ready data concepts (trusted datasets, lineage, quality thresholds, governed usage).