Senior Product Manager - Data & Analytics Platform

Job description

**Senior Product Manager, Data & Analytics Platform** **About the Role** We're looking for a Senior Product Manager to lead the evolution of our shared data foundation for customer-facing analytics and AI experiences. You will focus on our **Data Hub**—the platform that brings together data from products and systems across Intermedia, including UC/PBX, Contact Center, AI, messaging, and other services, and makes that data consistently available for analytics, reporting, APIs, and AI-driven use cases. You will work closely with the Principal Product Manager for Product Analytics, Product Managers across individual product domains, and our Engineering and Data teams to establish common patterns and best practices for how data is instrumented, brought into the Data Hub, modeled, governed, and made available for consumption. The goal is to create a scalable foundation that allows product teams to build analytics and AI experiences faster and more independently, without creating fragmented data pipelines and one-off solutions. **What You'll Do** - **Own the product strategy and roadmap for the Data Hub and shared analytics data foundation**, aligned with the broader Product Analytics strategy. - Partner with PMs and Engineering teams across UC/PBX, Contact Center, AI, messaging, and other domains to define and prioritize the roadmap for bringing product data into the Data Hub. - Establish and drive adoption of **common patterns and best practices for product instrumentation, event generation, data contracts, ingestion, and access**, making analytics requirements a standard part of product development rather than an afterthought. - Partner with Data and Engineering architects to define and evolve **common data models** for key concepts such as interactions, users, agents (human and AI), accounts, services, features, and utilization. - Ensure data from different products can be consistently connected and analyzed across domains, channels, and customer journeys rather than remaining in product-specific silos. - Define requirements for how trusted data is exposed to downstream consumers through APIs, analytics tools, embedded experiences, AI applications, and other access patterns. - Make it progressively easier for product and engineering teams to onboard data and build on the shared foundation **without requiring the central analytics team to implement every use case**. - Balance migration of existing/legacy data flows with new product requirements, identifying where existing data can be leveraged and where investment in the target architecture is required. - Identify common needs across product teams and prioritize shared platform capabilities that reduce duplicated effort and accelerate analytics delivery across the company. - Define and track success measures for Data Hub adoption, data availability, quality, timeliness, platform utilization, and reduction of time and effort required to enable new analytics use cases. - Ensure the data foundation supports emerging AI use cases, including AI Agents, Interaction Insights, prompt-based analytics, and other AI-driven experiences. - Work closely with the Principal Product Manager for Product Analytics and the cross-product Analytics SME community to ensure Data Hub priorities are driven by customer, partner, and product needs. **What We're Looking For** - 5+ years of Product Management experience, with meaningful experience in **data platforms, analytics platforms, developer platforms, or technically complex B2B SaaS products**. - Strong understanding of modern data concepts, including event-driven architectures, data ingestion and pipelines, data contracts, data modeling, APIs, data quality, and semantic/business models. - Ability to engage deeply with Data and Engineering teams—understanding technical trade-offs and challenging design decisions—while maintaining a strong focus on product and business outcomes. - Experience building shared platforms used by multiple product or engineering teams, with an understanding of **platform-as-a-product** principles. - Experience translating needs across multiple product domains into reusable platform capabilities rather than one-off solutions. - Proven ability to influence and align multiple teams without direct authority and to drive adoption of common standards and practices. - Strong prioritization skills and the ability to balance foundational platform investments, migration/technical debt, and immediate customer-facing needs. - Excellent written and verbal communication skills, with the ability to translate complex data architecture and platform concepts into clear product and business narratives. **Nice to Have** - Experience with Snowflake or similar modern cloud data platforms. - Experience with Kafka, EventHub, or other event-driven architectures. - Experience with Contact Center (CCaaS), Unified Communications (UCaaS), or communications platforms, particularly interaction, agent, call, messaging, or utilization data. - Experience supporting both **real-time and historical analytics** from a shared data foundation. - Experience with AI/ML products and the data requirements associated with AI Agents, interaction analytics, or generative AI applications. - Experience modernizing legacy analytics/data architectures while continuing to support existing customer-facing capabilities. **What Success Looks Like** - Product teams have a clear and consistent path for bringing new data into the Data Hub. - Key product domains progressively move from fragmented and legacy data flows toward the shared architecture. - Common data models allow information across UC, CC, AI, and other products to be connected and analyzed consistently. - Analytics and AI teams can access trusted product data without repeatedly building custom pipelines or relying on a central team for every new use case. - New products and features are designed with instrumentation and analytics requirements from the beginning. - The time required to make new product data available for customer-facing analytics and AI use cases decreases materially over time.