About the Role:Grade Level (for internal use):
11
The Team
We are a global but tight‑knit team driven by collaboration and enablement, focused on building scalable, enterprise‑grade platforms, services, and common capabilities that drive wins across our entire division. Our team values collaboration, end‑user empathy, hard work, and honesty—creating an environment where innovative solutions can flourish and make a meaningful impact at scale.
Responsibilities and Impact
- Drive product strategy and roadmap for critical data infrastructure components, including data onboarding, storage solutions, and core platform engines
- Lead cross-functional teams to deliver data engineering capabilities, admin utilities, and data quality solutions that enable enterprise-scale analytics
- Own product vision for disaster recovery and resiliency frameworks to ensure platform reliability and business continuity
- Define and execute an ontology integration strategy to enhance knowledge management excellence and semantic data capabilities
- Collaborate with engineering teams to implement MLOps practices for machine learning operations and model lifecycle management
- Develop and maintain an innersource ecosystem strategy to enable cross-team collaboration on core platform capabilities within defined governance guardrails
- Partner with stakeholders to define requirements for common data pipeline capabilities and shared tooling that data engineering teams utilize when building enterprise data workflows
- Champion data quality initiatives and drive technical implementation of data quality tools and monitoring solutions that ensure accuracy and consistency across all data processing workflows
What We’re Looking For:
Basic Qualifications
- Bachelor’s degree in Computer Science, Engineering, Data Science, or related technical field, or equivalent professional experience
- 8+ years of product management experience focused on data platforms, analytics infrastructure, or enterprise data solutions
- Hands-on experience with advanced Databricks features, including Delta Lake, MLflow, and Databricks SQL for end‑to‑end data and ML pipeline management
- Strong technical background with hands-on experience in data engineering technologies such as Apache Spark, Kafka, Airflow, or similar distributed processing frameworks
- Proven experience with cloud data platforms, including AWS, Azure, or Google Cloud Platform
- Understanding of Machine Learning Operations (MLOps) processes, including model deployment, monitoring, and lifecycle management
- Strong knowledge of standard Software Development Life Cycle (SDLC) methodologies, including Agile, Scrum, and DevOps practices
- Demonstrated ability to translate business requirements into technical product specifications while working closely with cross-functional engineering teams
Preferred Qualifications
- Advanced degree (MBA, MS in Computer Science, Data Science, or related field)
- Experience with data governance frameworks and regulatory compliance requirements in enterprise environments
- Background in financial services, fintech, or other regulated industries with a strong understanding of data privacy and security requirements
- Experience leading cross-functional teams and managing vendor or partner relationships within complex technical ecosystems
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 $100,000 to $149,000. 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 .
