Lead Data Taxonomist

The New York Times
New York, NYPosted 25 March 2026

Job Description

The mission of The New York Times is to seek the truth and help people understand the world. That means independent journalism is at the heart of all we do as a company. It’s why we have a world-renowned newsroom that sends journalists to report on the ground from nearly 160 countries. It’s why we focus deeply on how our readers will experience our journalism, from print to audio to a world-class digital and app destination. And it’s why our business strategy centers on making journalism so good that it’s worth paying for. About the Role, Mission or Department Overview The New York Times is looking for a Lead Data Taxonomist to serve as the architect and steward of our enterprise-wide event taxonomy. The New York Times is home to multiple products across News, Games, Cooking, Wirecutter, and The Athletic with billions of monthly events. Therefore, the need for a unified, governed, and high-quality data language is critical. In a long-term effort, it will dramatically improve data quality at the New York Times and power real-time recommendation engines, personalization algorithms, and future AI initiatives. You will be responsible for New York Times' global event data catalog and taxonomy. You will sit at the intersection of Product, Data Platform, and Analytics, capturing every interaction — from a Wordle guess to a subscriber payflow — through a standardized, semantic lens. You will report to our Executive Director, Data Insights. Responsibilities: You will develop and maintain a multi-domain enterprise taxonomy that includes Behavioral, Commerce, Messaging, User, Session, and other key data domains. This taxonomy will be transferable across all NYT products and codebases. You will establish and enforce global events, event properties, standard property values, required fields, acceptable value types, and naming conventions. You will own the definition and lifecycle of global events that track the user journey across the entire NYT ecosystem. You will be the final authority for the taxonomy, approving or rejecting all pull requests and proposals for new events, properties, or changes to the existing taxonomy. You will oversee the enterprise event catalog, ensuring it serves as a high-fidelity source of truth with accurate business logic and metadata. You will define and monitor domain-specific KPIs for data quality, security, and integration, solving for systemic issues. You will iterate with engineers to improve instrumentation capabilities in service of data products You will collaborate with engineers to align the taxonomy with Kafka infrastructure and with Analytics Engineering to ensure data is designed to flow effortlessly downstream and to help develop our larger New York Times ontology You will facilitate and provide oversight for data councils concerning events and metrics You will provide support and training to product and analytics teams to improve and evolve event tracking You will advocate for information architecture best practices and master data management (MDM) principles across the organization. Demonstrate support and understanding of our value of journalistic independence and a strong commitment to our mission to seek the truth and help people understand the world. Basic Qualifications: 7+ years in Data Taxonomy, Data Ontology, Data Architecture, Technical Product Management, or Digital Analytics Implementation. 5+ years of developing taxonomies and information architecture for front-end and back-end use cases 5+ years of experience with event-cataloging software (e.g., Avo, Iteratively, Segment Protocols, or proprietary internal tools). Experience managing complex, global event taxonomies for large, multi-product organizations Expertise with JSON, YAML, and other data modeling languages and tools Mastery of SQL, Python, and other data wrangling tools and languages Experience enforcing standards in high-growth, decentralized engineering environments Applied knowledge of indus ... (truncated, view full listing at source)
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