Data Quality Lead, Data Governance

Job Summary

The Data Quality Lead is a senior contributor within the A&I Data Governance team, bringing analytics fluency and deep governance ability to ensure BSWH’s data is trustworthy, harmonized, and ready for advanced analytics and AI. This role defines and operationalizes enterprise data quality standards across federated domains, partners closely with stewards, analytics, and MDM teams, and promotes transparent data incident management.

The ideal candidate is technically strong, strategically minded, and curious, comfortable experimenting with innovative approaches to continuously advance governance maturity and strengthen a culture of trusted, high‑quality data.

• Support enterprise data quality frameworks across federated clinical, operational, and financial domains by helping define standards, controls, and shared expectations for CDEs, clinical metrics, regulatory reporting, and AI‑ready data.
• Guide and enable data stewards and domain teams in using Ataccama ONE for data quality rule governance, glossary stewardship, metadata completeness, lineage visibility, issue logging, and domain accountability.
• Build and inform DQ monitoring approaches including dashboards, scorecards, and issue‑management structures that domains use to track quality, transparency, and stewardship performance.
• Partner with analytics, IT, and domain leaders to drive consistent adoption of DQ governance practices across federated teams, ensuring alignment with organizational priorities, regulatory expectations, and clinical/operational workflows.
• Collaborate with MDM governance teams to ensure high‑quality healthcare master data (Patient, Provider, Location, Encounter) through aligned standards for matching/merging, golden records, survivorship rules, and reference‑data stewardship.
• Support transparent incident reporting and root‑cause analysis by ensuring federated teams follow Ataccama‑based workflows and governance processes for documenting, evaluating, and resolving DQ issues.
• Communicate DQ risks and requirements clearly to domain stakeholders, highlighting impacts on patient safety, quality reporting, operational performance, and enterprise analytics/AI initiatives.
• Influence adoption of governance and DQ standards across analytic, clinical, and operational teams by reinforcing guardrails, stewardship responsibilities, and the value of trusted data.
• Find improvements to data quality and stewardship workflows, helping refine operating models and processes that enhance consistency, accountability, and transparency across federated domains.
• Mentor peers and junior team members to strengthen organizational literacy in data quality, metadata, lineage, and governance practices.
• Evaluate emerging tools and methods including GenAI‑supported DQ signals, anomaly detection for clinical measures, lineage automation, and metadata enrichment to recommend enhancements to the enterprise DQ framework.
• Monitor trends in data governance, healthcare data quality maturity, and AI safety, integrating relevant advancements into DQ standards, stewardship practices, and Ataccama governance patterns.

• Interprets and communicates data quality risks and lineage implications clearly across clinical, operational, and technical stakeholders, enabling informed decision‑making in a federated model.
• Influences stewardship adoption of Ataccama‑based workflows, metadata standards, and data quality expectations across domains with effective communication and relationship‑building skills.
• Connects data quality governance to organizational priorities, including patient safety, regulatory compliance, analytics reliability, and AI/ML readiness.
• Collaborates effectively across analytics, IT, clinical, operational, and MDM teams, resolving ambiguity and guiding alignment on quality standards and governance guardrails.
• Demonstrates continuous improvement and curiosity, exploring emerging capabilities (GenAI‑supported DQ signals, anomaly detection, metadata enrichment, lineage automation) to strengthen governance maturity and steward effectiveness.

• With MDM platforms/processes…