Senior Technical Product Manager, Data Engineering & Data Science Solutions

S&P Global•Boulder, CO

5d•Onsite

About The Position

The Senior Technical Product Manager, Data Engineering & Data Science Solutions will drive product strategy and roadmap for critical data infrastructure components, including data onboarding, storage solutions, and core platform engines. This role involves leading cross-functional teams to deliver data engineering capabilities, admin utilities, and data quality solutions that enable enterprise-scale analytics. The position also owns the product vision for disaster recovery and resiliency frameworks, defines and executes an ontology integration strategy, and collaborates with engineering teams to implement MLOps practices. Additionally, the role involves developing and maintaining an innersource ecosystem strategy and partnering with stakeholders to define requirements for common data pipeline capabilities and shared tooling. A key aspect of this role is championing data quality initiatives and driving the technical implementation of data quality tools and monitoring solutions.

Requirements

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
Bachelor’s degree in Computer Science, Engineering, Data Science, or related technical field, or equivalent professional experience

Nice To Haves

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

Responsibilities

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