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

Requirements

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.

Benefits

Health & Wellness: Health care coverage designed for the mind and body.
Flexible Downtime: Generous time off helps keep you energized for your time on.
Continuous Learning: Access a wealth of resources to grow your career and learn valuable new skills.
Invest in Your Future: Secure your financial future through competitive pay, retirement planning, a continuing education program with a company-matched student loan contribution, and financial wellness programs.
Family Friendly Perks: It’s not just about you. S&P Global has perks for your partners and little ones, too, with some best-in class benefits for families.
Beyond the Basics: From retail discounts to referral incentive awards—small perks can make a big difference.

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

data onboardingstorage solutionsdata engineeringMLOpsontology integrationdata quality toolsApache SparkKafkaAirflowDatabricks

Soft Skills

leadershipcollaborationcommunicationstrategic thinkingproblem-solvingstakeholder managementcross-functional teamworktechnical specification translationgovernancebusiness continuity