Sr. Technical Product Manager

EagleView

10d•$110,000 - $157,600•Remote

About The Position

EagleView, the leader in aerial imagery and geospatial intelligence, is seeking a Sr. Technical Product Manager in Data & AI to define and scale the company’s Geospatial Content Platform. This role is accountable for identifying, defining, and documenting platform requirements for ML-derived datasets, data taxonomy, and enterprise-scale distribution models. The Sr. TPM will shape how AI-generated data and insights are structured, versioned, governed, and distributed via APIs, MCP, Agents, and ecosystem integrations. This role will operate at the intersection of data engineering, machine learning, platform architecture, and go-to-market enablement to ensure scalable, secure, and monetizable AI-native capabilities.

Requirements

8+ years in Technical or Platform Product Management, preferably in data or AI platforms.
Strong understanding of: ML lifecycle and productionization Data engineering and distributed systems API-first platform design Data modeling and schema governance
Experience defining requirements for enterprise-scale data platforms and AI-integrated systems.
Familiarity with cloud platforms (AWS or equivalent).
Demonstrated ability to translate complex technical systems into structured, actionable requirements.
Strong analytical thinking, business acumen, and executive-level communication skills.

Nice To Haves

Background in geospatial data, imagery, or 3D modeling.
Experience with SQL/NoSQL systems, vector databases, feature stores, RAG architectures, and agent frameworks.
Experience with MCP or AI protocol integrations.
Experience building data monetization platforms (DaaS, APIs, subscription models).
Familiarity with ontology design and cross-domain data normalization.
Experience supporting enterprise customers at scale.

Responsibilities

Define roadmap and requirements for the Content Platform supporting ML-derived content.
Establish requirements for data taxonomy, schema governance, versioning, and lineage.
Translate AI research outputs into production-grade, enterprise-ready platform capabilities.
Own creation of PRDs and structured requirements for ML derived features and attributes, confidence scoring, and quality frameworks.
Partner with ML, Data Engineering, and Architecture teams to ensure NFRs such as performance and availability are well defined.
Define content distribution requirements driving the evolution of our APIs, geospatial data pipelines, event-driven workflows, MCP integrations, and agent-based consumption models.
Ensure platform scalability, performance, security, and multi-tenant readiness.
Support platform capabilities that enable monetization models including Data-as-a-Service and API enablement.
Engage go-to-market product managers and internal stakeholders to identify integration and AI workflow requirements.
Translate vertical use cases into reusable platform capabilities and POVs (proof-of-value).
Define success metrics across adoption, quality, latency, and uptime.
Establish telemetry, observability, and SLAs for platform performance.
Lead backlog prioritization across platform initiatives.
Align research, engineering, and go-to-market stakeholders on scalable delivery.
Communicate roadmap, releases, and impact to leadership.