Sr. Technical Product Manager

Jobgether

3d•$110,000 - $157,600•Remote

About The Position

In this role, you will lead the development and evolution of a cutting-edge data and AI platform that powers advanced geospatial insights and enterprise-scale data products. You will work at the intersection of product strategy, data engineering, and machine learning to transform AI-generated outputs into scalable platform capabilities. This position involves shaping how complex datasets are structured, governed, and distributed through APIs and modern data ecosystems. Collaborating closely with engineering, research, and go-to-market teams, you will drive initiatives that enable secure, high-performance, and monetizable data services. The role offers an opportunity to influence platform architecture, guide product strategy, and deliver impactful solutions for enterprise customers. It is ideal for a technically minded product leader passionate about building data-driven platforms and AI-enabled products.

Requirements

8+ years of experience in Technical or Platform Product Management, ideally focused on data platforms, AI systems, or cloud technologies.
Strong understanding of machine learning lifecycle and productionization processes.
Deep knowledge of data engineering concepts, distributed systems, and API-first platform design.
Experience defining requirements for enterprise-scale data platforms and AI-integrated architectures.
Familiarity with data modeling, schema governance, and data lifecycle management.
Experience working with cloud platforms such as AWS or equivalent environments.
Strong ability to translate complex technical systems into clear product requirements and strategic roadmaps.
Excellent analytical thinking, communication, and stakeholder management skills, including the ability to engage senior leadership.

Nice To Haves

Experience with SQL/NoSQL databases, vector databases, feature stores, or modern AI architectures is a plus.
Background in geospatial data, imagery platforms, data monetization models, or enterprise SaaS ecosystems is advantageous.

Responsibilities

Define and drive the product roadmap for a scalable data and AI platform, focusing on ML-derived datasets and enterprise distribution models.
Establish data taxonomy, schema governance, versioning, and lineage requirements to support structured and reliable data ecosystems.
Translate machine learning research outputs into production-ready platform capabilities and enterprise-grade data products.
Develop product requirement documents (PRDs) for AI-generated features, including attributes, quality frameworks, and confidence scoring mechanisms.
Collaborate with data engineering, architecture, and ML teams to ensure performance, availability, and scalability requirements are clearly defined.
Define requirements for API-first distribution models, geospatial data pipelines, event-driven workflows, and AI integration frameworks.
Enable data monetization strategies such as Data-as-a-Service (DaaS) and API-based consumption models.
Partner with product, go-to-market teams, and internal stakeholders to translate industry use cases into scalable platform capabilities.
Establish success metrics, telemetry, and observability standards to measure adoption, performance, latency, and reliability.
Lead cross-functional alignment and backlog prioritization, ensuring smooth collaboration between engineering, research, and business teams.