Senior Product Manager - Data Foundation

About the Role We’re looking for a Product Manager – Data Foundation to drive the strategy and development of our core data infrastructure and data science platform. This role is central to building a robust data foundation that enables analytics, machine learning, and defines the roadmap for the Data Science Platform. Partnering closely with engineering, data science, and product stakeholders, you’ll translate user needs and business intelligence into prioritized features, drive delivery, and ensure the platform enables rapid, reliable model development and productionization. Responsibilities • Own the data foundation and data science platform roadmap — including data architecture, ingestion pipelines, governance, metadata management, and data science product line. • Partner with data engineers, analytics, and business teams to design and deliver scalable data solutions supporting multiple business domains. • Engage with Data Scientists, ML Engineers, and stakeholders to gather requirements, validate pain points, and prioritize features. • Work daily with engineering teams to drive implementation, remove blockers, and ensure timely delivery. • Define and implement data product principles (data as a product, data contracts, lineage, and quality). • Prioritize initiatives that improve data availability, discoverability, and trustworthiness across the company. • Set and monitor KPIs around data quality, latency, time-to-prototype, and model deployment frequency. • Champion data governance and standardization efforts to ensure compliance and consistency. • Drive alignment between data engineering and product teams to enable self-service analytics and accelerate delivery of insights. Qualifications • Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related field. • At least 5 years of experience in Product Management with a focus on data infrastructure (e.g., Kafka, Airflow, dbt, Snowflake, BigQuery) and data science platform (feature engineering, training, model evaluation, deployment, monitoring). • Technical fluency with data platforms, MLOps concepts (CI/CD for ML, model registries, feature stores), containerization, orchestration (Kubernetes), and cloud services (AWS, GCP, Azure). • Proven experience in managing the data lifecycle — from ingestion to storage, governance, and consumption. • Strong analytical mindset and ability to work with SQL and data visualization tools for validation and insights. • Demonstrated success in working with cross‑functional engineering teams in an Agile environment. • Excellent communication and stakeholder‑management skills — able to bridge technical and business perspectives. • Experience implementing data mesh or data platform modernization initiatives. #J-18808-Ljbffr