Senior Product Manager - Data & Analytics Platform

Department: Product

Employment Type: Full Time

Location: Portugal, Portugal, Portugal, Remote

• 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.

• 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.

• 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.

• 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.

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