Senior Technical Product Manager, Data Engineering Solutions

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 and platform capabilities, including data onboarding, storage solutions, and core platform services Lead cross-functional teams to deliver shared data engineering capabilities that enable scalable, enterprise-wide analytics and AI solutions Partner with stakeholders to define requirements for common data platform capabilities, pipeline tooling, and developer experiences used across enterprise data workflows Own product vision for platform resiliency, disaster recovery, and operational readiness to ensure reliability, scalability, and business continuity Collaborate with engineering teams to evolve machine learning and AI platform capabilities, including support for model lifecycle management and MLOps practices What We’re Looking For:

7+ years of experience in product management, or a combination of product management and technical roles, delivering complex software, platform, data, or infrastructure solutions Strong understanding of cloud-native architectures, software platforms, and enterprise technology ecosystems Familiarity with modern data platforms such as Databricks, Snowflake, or similar technologies used for large-scale data processing and analytics Familiarity with data platforms, data pipelines, analytics workflows, or related technical concepts Working knowledge of software development lifecycle (SDLC) methodologies, including Agile and DevOps practices Ability to engage effectively with software, platform, and data engineering teams on technical architecture, platform capabilities, and product tradeoffs Strong communication and stakeholder management skills, with the ability to influence engineers, architects, data practitioners, and business leaders Proven ability to define product vision, prioritize investments, and deliver measurable business outcomes Bachelor's degree in Computer Science, Engineering, Information Systems, Data Science, or a related field, or equivalent practical experience Preferred Qualifications Experience working with Databricks technologies, including Delta Lake, MLflow, Databricks SQL, or similar lakehouse capabilities Experience supporting data engineering, analytics, machine learning, AI, or platform products Understanding of machine learning concepts, model lifecycle processes, and MLOps practices Prior experience in software engineering, data engineering, analytics engineering, or related technical disciplines Experience with cloud platforms such as AWS, Azure, or Google Cloud Platform Exposure to enterprise data quality, resiliency, governance, metadata management, or related platform initiatives Experience working with internal platforms, developer platforms, or shared enterprise capabilities Advanced degree in a technical or business-related discipline 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 $110,000 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 eligible to receive additional S&P Global benefits. For more information on the benefits we provide to our employees, please click  here .     About S&P Global Energy At S&P Global Energy, our comprehensive