Senior Principal Product Manager - Data Platform

Dow Jones•New York, NY

23h•$175,000 - $215,000•Hybrid

About The Position

As Senior Principal Product Manager - Data Platform, you will be the portfolio owner of our data and content engineering platform within the Technology department. You will set the visionary strategy for the core data systems that all Dow Jones data and content flow through. In this highly strategic role, you will act as a partner to business, engineering, and newsroom leads - translating complex challenges into a clear enterprise data architecture. You will report to the VP of Product Management - Platforms. Our Technology team drives the evolution of our Technology, Engineering, Data, Product and User Experience functions. With a keen focus on delivering cutting-edge solutions, we shape the digital landscape for our customers, readers and users. From revolutionizing visuals to optimizing tools and harnessing the power of data, mobile, video and social platforms, our team is committed to providing a seamless and immersive experience across all touchpoints. Collaborating closely with our newsrooms and strategic partners, we spearhead the development of groundbreaking products and technologies.

Requirements

12+ years’ product management experience overseeing complex data engineering platforms, content pipelines, or data infrastructure at scale
Advanced technical knowledge of modern data stack architecture, specifically Snowflake, data lakes, and analytics infrastructure
Deep domain expertise in enterprise data governance, regional privacy compliance (including European user constraints), and DataOps
Technical proficiency in pipeline orchestration, compute optimization, and semantic modeling
Bachelor's degree in Computer Science, Information Systems, Business, or equivalent experience

Nice To Haves

Background in media, information services, or content-rich businesses
Direct experience architecting data layers that support internal AI agents or ML models

Responsibilities

Drive Systemwide AI Readiness: Build the scalable, trusted, and well-governed data foundation (including the data lake, Snowflake ecosystem, and data governance)
Transform Internal Decision-Making: Strengthen the analytics engineering and semantic layer
Optimize Organizational Execution: Audit and rationalize a sprawling platform backlog
Establish Rigorous Decision-Making Frameworks: Cut low-leverage work and translate downstream needs into clear, unified platform paradigms