Senior Manager, Data Quality

• Own and drive the enterprise strategy for data monitoring, quality standards, and anomaly detection across large-scale pharma and healthcare datasets including claims, EMR, lab, PSP, onboarding, SDOH, and related sources.
• Provide thought leadership and expert oversight on dataset architecture, schemas, transformations, lineage, and business rules to proactively identify systemic quality risks and improvement opportunities.
• Define, govern, and continuously evolve enterprise-wide DQ rules, anomaly detection frameworks, thresholds, and monitoring controls aligned to business criticality and risk profiles.
• Lead complex, high-impact root-cause analyses (RCA) for critical data issues and recurring quality failures; guide Data Enablement, Data Operations, Engineering, and vendors toward sustainable remediation and prevention.
• Establish and standardize enterprise data quality dimensions, metrics, and controls across accuracy, completeness, timeliness, conformity, uniqueness, and referential integrity.
• Design and govern scalable DQ frameworks, rule lifecycle management, and monitoring standards, ensuring consistency, reusability, and audit readiness across platforms and domains.
• Provide strategic direction for DQ dashboards, scorecards, and executive-level reporting; ensure monitoring outputs enable proactive decision-making and operational excellence.
• Define and track KPIs for data quality performance, rule effectiveness, SLA adherence, and business impact; translate insights into prioritized improvement roadmaps.
• Act as a senior partner to DQ, DEO, IT, Governance, DS/IDS, and business leadership to align on quality expectations, investment priorities, and roadmap execution.
• Lead intake, prioritization, and governance forums for new datasets, standards, and monitoring enhancements, balancing business value, risk, scalability, and resource constraints.
• Champion a standards-driven, prevention-first data quality culture across teams, promoting best practices, clarity of accountability, and continuous improvement.

• 10+ years of experience in Data Quality, Data Governance, Data Engineering, or Data Management, preferably within pharma or healthcare environments.
• Demonstrated experience defining enterprise data quality strategies, frameworks, and standards with measurable impact on data reliability and business outcomes.
• Deep expertise in SQL (Snowflake, Oracle, MS SQL Server) with strong understanding of large-scale data architectures and pipelines.
• Extensive experience with modern data platforms and orchestration tools such as Databricks, Dataiku, Azure Data Factory, or equivalent ecosystems.
• Strong understanding of monitoring automation, anomaly detection approaches, and scalable DQ framework design.
• Familiarity with BI and visualization tools (Power BI, Tableau) for executive and operational quality reporting.
• Strong domain understanding of pharma/healthcare datasets and regulatory expectations (preferred)

• Proven ability to lead through influence, setting direction and driving adoption of standards across diverse technical and business teams.
• Exceptional analytical thinking and structured problem-solving skills, with the ability to simplify complex data risks into clear executive narratives.
• Strong stakeholder management, communication, and documentation skills, capable of engaging senior leadership and guiding enterprise-level decisions.

Novartis