We're looking for a Lead Product Manager to build and scale a first-of-its-kind partner platform and data ingestion ecosystem. This is a hands-on leadership role, you'll own the strategy and get into the details, moving between executive alignment and sprint-level execution depending on what the week calls for. The core of the work is two-sided: building a partner-facing platform that drives real transaction volume, and engineering the backend infrastructure to capture, structure, and enrich high-fidelity spatial and media assets at scale. The data flowing through this platform will feed AI model training and unlock new enterprise revenue streams. You'll be the connective tissue between executive vision, engineering reality, and the legal and financial guardrails that make it all defensible. At this level, this role requires broad expertise across several disciplines: product, data engineering, commercial strategy, and cross-functional leadership and the independent judgment to navigate significant ambiguity. You'll be accountable for outcomes that impact the broader organization, not just your immediate team. What you'll do: Roadmap Delivery & Execution Own the product lifecycle and roadmap for our partner ecosystem and data pipeline. You'll translate business goals, specifically around data ownership and asset rights into a sequenced build plan that balances speed-to-market with long-term defensibility. That means writing crisp PRDs, running sprint planning, and staying close enough to engineering to catch problems before they become blockers. You'll apply independent judgment in determining methods and sequencing, and your decisions will have real impact on the function. API & Ingestion Engineering Define the product requirements for our core ingestion channels: bulk upload APIs, automated system syncs, and batch pipelines. The goal is to replace fragmented, manual data handoffs with a clean, secure chain of custody for every asset that enters the platform. You'll also own the rights-tagging infrastructure which is the layer that determines what's legally cleared for AI training and derivative content creation downstream, including emerging computer vision pipelines for next-gen media offerings. Data Taxonomy & Standardization Work with Engineering and Data Science to establish a consistent metadata taxonomy including asset classification, attribute tagging, geolocation and make sure every asset flowing in is structured and rights-cleared from the moment it arrives. This requires evaluating intangible factors and making judgment calls on taxonomy design that will shape how the entire platform scales, including how well it supports model training pipelines as our AI capabilities mature. Coverage of tagged, AI-ready assets is a core platform KPI you'll own. Algorithmic Mechanics & Ecosystem Incentives Design the partner discovery and ranking logic so the platform naturally rewards higher-quality, more valuable capture formats. Think carefully about the give-get across partner horizons including what partners receive now versus what they unlock over time and make sure the mechanics actually produce the behavior you're after. This requires conceptual thinking about incentive structures and their second-order effects on ecosystem health. Commercialization & Cross-Functional Operations Work closely with Legal to make sure vendor onboarding and contract terms translate directly into platform guardrails including exclusivity structures, data transfer rights, retention clauses, and succession terms all need to live somewhere in the product, not just in a filing cabinet. With Finance and Ops, help scale the payment architecture and commercial pipelines. You'll coordinate across multiple groups and regularly persuade senior, non-technical stakeholders on complex matters. As the asset library matures, support in building the infrastructure needed for enterprise licensing and potential M&A.. Enterprise Monetization Enablement As the data library grows, build the scaffolding to aggregate, structure, and package it for high-margin enterprise use cases for financial institutions, risk underwriting, institutional analytics, and advanced computer vision applications for next-generation media products. This role focuses on platform readiness, not sales/partnership execution. Your job is to make sure the infrastructure can support those conversations when the business is ready to have them. What you'll bring: 30+ years of related experience with a Bachelor's degree; 8+ years with a Master's degree; or a PhD with 5+ years in product management, with a track record delivering complex B2B/B2C marketplaces, API platforms, or enterprise data ingestion systems from early build through scale. 30 expertise across several related disciplines including product strategy, data engineering, commercial operations, and legal/compliance frameworks. Able to lead across these areas without needing a hand-off at every boundary. 30 technical fluency. You're comfortable in the weeds with data pipelines, API design, and metadata schemas. 30 ability to evaluate complex situations where the right answer isn't obvious, including commercial contracts, partner dynamics, and build vs.buy decisions, and exercise independent judgment to chart a course. 30 clear, persuasive communicator who can adapt their style across technical teams, legal counsel, finance leadership, and C-suite audiences often in the same week. Strongly Preferred 30 experience with machine learning workflows, computer vision, or 3D/spatial data, especially around model training pipelines or large-scale media/image processing for AI applications. 30 formal network builder. You've created lasting cross-functional working relationships, not just one-off alignments, and you're recognized internally as a subject matter expert others turn to. 30 in prop-tech, real estate technology, digital media licensing, or two-sided marketplace infrastructure. What the First Year Looks Like These milestones reflect a trajectory, not a rigid checklist. The expectation is that someone strong moves through the role roughly like this: Early on, you're in listening and learning mode getting deep with Engineering, Legal, and our initial partners to understand what's actually true versus what's assumed. By the end of Q1 or earlier, you've got a clear-eyed PRD, user stories, and an API schema blueprint that reflects reality, and you've synthesized early demand signals from an initial validation test to sharpen the core build plan. By mid-year, the alpha platform is live. Initial partners are ingesting through direct pipelines rather than manual handoffs, and assets are flowing in tagged, structured, and rights-cleared. It won't be perfect, but it works and you know exactly what to fix next. Into the back half, you're tuning the ecosystem. Ranking and incentive mechanics are live, and you can see partner behavior starting to shift toward higher-quality, higher-fidelity formats. You're iterating on what the data tells you, not just instinct, and adoption of premium capture options is measurably growing across the partner network. By end of year, metadata mapping is largely automated and the asset library is structured enough that AI and enterprise teams can actually start using it. The platform hasmoved from a build project to a running business and the foundation is in place for downstream licensing and model training.