About the Role:

Grade Level (for internal use):

S&P Global Ratings

The Role: Director Data Quality Engineering

Location: NYC or NJ (Hybrid 2 days onsite a week)

What’s in it for You? 

This is a high-impact senior leadership role where you will: 

• Build and scale the blueprint for data and AI quality engineering across the enterprise. 

• Ensure lineage, migrations, and transformations deliver accuracy, compliance, and trust. 

• Lead the data service team at the intersection of data, AI, customer experience, and engineering excellence. 

• Shape the future of enterprise data quality and AI assurance, driving measurable outcomes in customer trust and delivery speed. 

As the Director of Quality Engineering (Data Services & Data Science), you will: 

• Drive enterprise-wide QE strategy across ELT pipelines, data transformations, migrations, cataloging, and observability. 

• Ensure AI/ML models are validated for accuracy, fairness, robustness, and compliance. 

• Embed engineering excellence into the data lifecycle, making quality a continuous capability. 

• Partner across data engineering, data science, product, and compliance to elevate customer experience and delivery speed. 

Key Outcomes 

Enterprise QE for Data & AI 

• Define and execute QE strategy for Data Services, Data Science, and Core Data Platforms. 

• Drive adoption of data lineage and cataloging frameworks for traceability and compliance. 

• Lead quality strategies for ETL/ELT workflows, transformations, and migrations. 

• Oversee frameworks for reconciliation, anomaly detection, and schema validation. 

AI & Data Science Quality 

• Establish robust validation practices for AI/ML models (accuracy, fairness, transparency). 

• Partner with Data Science teams to ensure responsible AI through repeatable methods. 

• Drive autonomous testing and AI-augmented QE for scalable validation. 

Engineering Excellence & Innovation 

• Champion observability and continuous quality in cloud-native data platforms (AWS, Azure). 

• Promote automation, governance, and reusable frameworks. 

• Foster innovation via synthetic data and intelligent simulation tools. 

Global Leadership & Collaboration 

• Lead and mentor a global QE team, fostering innovation and accountability. 

• Partner with engineering, product, DevOps, and compliance leaders for end-to-end ownership. 

• Influence cross-functional strategy with scalable frameworks and governance. 

Measurement & Value Delivery 

• Define KPIs for data quality, lineage accuracy, AI reliability, and QE maturity. 

• Align QE practices with emerging tech, regulations, and business priorities. 

• Deliver measurable impact in data trust, customer satisfaction, and delive