Grade Level (for internal use):

The Team:

This team is essential to the division's data strategy, playing a key role in data transformation and leading efforts to replace legacy technical debt. The team embodies a strong commitment to delivering on business needs and demonstrates exceptional collaboration with both product and business partners. They are known for their collaborative approach, strong technical expertise, and the high energy and dedication they bring to their work.

Responsibilities and Impact:

• Lead and scale a world-class engineering organization of 100+ data engineers, ML engineers, and platform developers, including managing managers and Staff/Principal-level engineers while driving hiring, retention, and talent development strategies
• Provide strategic technical direction for enterprise-scale lakehouse architecture using Databricks Data Intelligence Platform (Unity Catalog, Delta Lake, Delta Live Tables, Mosaic AI) and AWS cloud services, ensuring highly performant, cost-effective, and interoperable data foundations
• Drive the organization's transition to production-grade autonomous AI systems, leveraging Databricks AI stack including MLflow, Feature Store, Vector Search, and Model Serving to deliver measurable business value beyond proof-of-concept implementations
• Establish rigorous engineering standards and operational excellence practices including SLOs, error budgets, incident management, and infrastructure-as-code using tools such as Terraform and Databricks Asset Bundles for reliable platform delivery
• Oversee enterprise semantic modeling and RAG architecture strategies, ensuring AI systems are grounded in Unity Catalog-governed data assets with proper access controls, lineage tracking, and auditability to prevent hallucinations
• Foster a high-performance engineering culture rooted in psychological safety, continuous learning, and rapid experimentation while partnering with Product, FinOps, and executive leadership to translate business goals into actionable technical roadmaps

Compensation/Benefits Information: 

(This section is only applicable to US candidates) S&P Global states that the anticipated base salary range for this position is $185,000 to $265,000. Final base salary for this role will be based on the individual’s geographic location, as well as experience level, skill set, training, licenses and certifications. 

In addition to base compensation, this role is eligible for an annual incentive plan. This role is not eligible for additional compensation such as an annual incentive bonus or sales commission plan. This role is eligible to receive additional S&P Global benefits. For more information on the benefits we provide to our employees, please click . 

What We're Looking For:

Basic Required Qualifications:

• 15+ years of engineering experience with 5+ years directly managing and scaling large engineering teams at Director or Head of Engineering level, including experience managing managers and guiding Staff/Principal-level individual contributors through organizational transformation
• Deep expertise in Databricks Data Intelligence Platform including Unity Catalog, Delta Lake internals, Delta Live Tables, Databricks Workflows, MLflow, and Mosaic AI, with ability to engage in technical discussions and provide strategic guidance on complex platform trade-offs
• Extensive experience architecting enterprise-scale data platforms on AWS including services such as Amazon S3, AWS Glue, Amazon EMR, AWS Lake Formation, with strong understanding of multi-region deployment strategies and security patterns (IAM, VPC, KMS)
• Proven technical depth in Apache Iceberg internals, schema evolution, and cross-platform table strategies, combined with hands-on experience in autonomous AI agent deployment and RAG pipeline architecture for production environments
• Demonstrated ability to build and scale engineering organizations from 20+ to 100+ engineers while establishing rigorous engineering standards, agile delivery practices, and operational SLAs for complex data and AI products
• Strong leadership and communication skills with experience translating complex technical trade-offs to non-technical stakeholders and influencing executive decision-making through data-driven recommendations and ROI analysis

Additional Preferred Qualifications:

• AWS and/or Databricks certifications with experience partnering with FinOps and cloud platform teams to optimize cloud consumption, performance, and platform ROI across multi-cloud data platform strategies
• Experience with semantic modeling, knowledge graphs, and enterprise data governance frameworks including metadata management and secure data sharing patterns at enterprise scale
• Background in LLMOps, model evaluation, and prompt orchestration with familiarity integrating AWS AI/ML services such as Amazon SageMaker and Amazon Bedrock into broader enterprise AI platform strategies
• Experience with infrastructure-as-code tools such as Terraform, CI/CD pipelines, and deployment automation including Databricks Asset Bundles for repeatable, auditable platform delivery across distributed cloud environments