Go-To-Market Data and AI Product Management Principal, Equinix

• 4-6 years in product management or technical program management or software engineering
• Prior experience as a data analyst, data scientist, software engineer, or similar technical role
• Proficient in SQL- can write complex queries, or programming languages, troubleshoot performance issues, and evaluate whether a proposed data model will serve the use case
• Familiar with ETL/ELT concepts and data pipeline architecture
• Comfortable operating with incomplete information and competing priorities; able to engage credibly with engineering on technical tradeoffs
• Strong communication skills; able to translate between technical and business audiences
• Familiarity with data quality challenges: entity resolution, duplicate handling, schema conflicts across systems
• Cloud data platform expertise (e.g., BigQuery, Snowflake, Databricks, Redshift)
• Able to break down ambiguous problems into clear requirements and actionable work items
• Able to quickly T-shirt size level of effort for data and AI initiatives across product and engineering
• Able to effectively translate between engineering and business - simplifying technical tradeoffs for executives while maintaining credibility with engineers
• Takes ownership of outcomes, follows through on commitments and holds partners accountable

Desirable

• 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 build toward commercializing differentiated data and insights offerings for external customers
• We're looking for a Principal, Data Product Management to drive the execution of the data and AI product roadmap for our Go-To-Market Organization
• 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, ensure products meet business needs
• This role resides within Equinix’s Enterprise Data & Analytics team, and reports to the Senior Director of Data Product, Go-To-Market
• 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 tradeoffs 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 stakeholder 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