Senior Product Manager, Data Platform – Intelligence

Responsibilities

• Own the end-to-end roadmap for Invoca's data platform and intelligence products
• Define and launch intelligence products – signals, scores, and insights – that create measurable value for both internal teams and paying customers
• Define how context is structured, staged, and delivered to AI agents across voice, SMS, and web – ensuring every interaction is grounded in the right data at the right time
• Own the intelligence and data products that other teams and customers use to build unique experiences on top of Invoca data
• Drive cross-team execution across teams working on the agentic platform, conversational AI products on voice and SMS, signals, and insights – managing dependencies, driving prioritization, and keeping work unblocked
• Build data applications and agents on our data lake that power product use cases across the business – from agent performance measurement to customer outcome analytics
• Partner with data engineers and ML teams to make principled tradeoffs across data freshness, coverage, latency, and cost
• Define success metrics for data products and continuously measure impact through adoption, coverage, agent performance, and quality signals
• Evaluate build vs. buy decisions for data infrastructure and guide vendor integrations behind stable, well-defined contracts
• Stay current on advancements in retrieval, grounding, and context injection patterns – translating signal into pragmatic product decisions

• 5+ years of product management experience, with meaningful time in data platforms, AI infrastructure, analytics products, or intelligence products
• Technical fluency sufficient to partner closely with data engineers and AI teams – comfortable in architecture discussions, familiar with data modeling, pipeline design, and schema evolution
• Hands‑on experience working with data lake or data warehouse platforms (e.g., Snowflake, Big Query, Redshift, or similar) – you understand how data products are built, governed, and consumed at scale
• Experience building intelligence products – signals, scores, insights, or recommendations – that both internal teams and external customers depended on
• Familiarity with evaluation approaches for data‑driven AI systems – you have a point of view on how to know whether a context or signal pipeline is actually working
• Experience in enterprise SaaS or compliance‑heavy domains where data governance, auditability, and multi‑tenant isolation are core design constraints, not afterthoughts
• Strong execution ownership – you drive prioritization, own cross‑team dependencies, and unblock your team without waiting to be asked
• Ability to move fast with imperfect information – this is a fast‑moving AI environment; the bar is calibrated judgment, not perfect context
• Clear communicator who can translate complex data and AI concepts for both engineering and business stakeholders without losing precision on either side
• Track record of making principled tradeoffs across data quality, latency, cost, and reliability in complex systems.