Data Product Management Principal

Who are we?

Equinix is the world’s digital infrastructure company, shortening the path to connectivity to enable the innovations that enrich our work, life and planet.

A place where bold ideas are welcomed, human connection is valued, and everyone has the opportunity to shape their future. A career at Equinix means being at the center of shaping what comes next and amplifying customer value through innovation and impact. You’ll work across teams, influence key decisions, and help shape the path forward. You’ll find belonging, purpose, and a team that welcomes you – because when you feel valued, you’re empowered to do your best work.

Our data products power the insights, decisions, and operations of teams across Equinix. We build and manage core data platform capabilities, reusable data models, semantic layers, decision‑ready dashboards, conversational BI, and custom AI solutions, including interconnection recommendation engines, automated data‑center cage design, etc. As we scale, we’re transforming internal operational and analytical data into unified, governed, high‑quality assets for Equinix, and we plan to move toward commercializing differentiated data and insights offerings for external customers.

We’re looking for a Data Product Management Principal to drive the execution of the data and AI product roadmap for our Global Markets and Product Organization (GMPO). This is a hands‑on technical product management role that bridges business needs and technical delivery. You will engage with business stakeholders to understand their pain points, and work closely with engineering and data science teams to translate business needs into well‑defined data and AI products, manage delivery timelines, and ensure products meet business needs.

This role resides within Equinix’s Enterprise Data & Analytics team, and reports to the Senior Director of Data Product, GMPO and Business Operations.

Drive product execution and delivery

• Own day‑to‑day product management for assigned data and AI products; manage backlogs, define requirements, and drive delivery
• Translate business needs into detailed product requirements and user stories that engineering can execute against
• Review and assess data models, pipeline designs, and system architectures; identify risks and trade‑offs before they become problems
• Guide engineering toward pragmatic solutions when requirements are ambiguous or shifting
• Identify and manage risks in release delivery and communicate with stakeholders with clarity and mitigation plans

Drive business impact through stakeholder engagement

• Build trust‑based relationships with business stakeholders by understanding their strategy, pain points, and how data and AI solutions can help
• Set clear expectations, push back on low‑value requests, and educate stakeholders on what’s possible and what’s not
• Frame ambiguous problems, generate hypotheses, and drive to recommendations when there’s no clear precedent

Improve operational excellence

• Support data product continuity during a major technology stack migration; ensure critical reporting capabilities remain intact as systems transition over multiple quarters
• Partner with engineering to design interim solutions that bridge old and new data models during migration
• Balance strategic roadmap work with operational responsibilities; protect time for building while keeping the lights on for products in “maintenance” mode
• Manage data quality escalations and work with engineering to resolve issues before they erode user trust
• Build processes that reduce repeat firefighting – if the same thing breaks twice, fix it permanently

Required

• 4‑6 years in product management for data and analytics products, or AI/ML products
• Prior experience as a data analyst, data scientist, data engineer, or similar technical role
• Proficient in SQL – can write complex queries, troubleshoot performance issues, and evaluate whether a proposed data model will serve the use case
• Can evaluate a data model and identify problems – familiar with star schema, normalization trade‑offs, and how schema design affects query performance
• Familiar with ETL/ELT concepts and data…