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Salary: USD 215,000 - 225,000 / annual
Zeitview is the leading intelligent aerial imaging company for high-value infrastructure, serving customers in solar, wind, insurance, construction, real estate, and critical infrastructure. The company provides actionable, real-time insights to help businesses recover revenue, reduce risk, and improve build quality, operating in over 70 countries.
In this Staff Technical Product Manager role, you will own the platform that powers every Zeitview product. Your core responsibility is to define and evolve the shared platform services and data capabilities that enable data capture, processing, analysis, and delivery across Zeitview's products and verticals. You will own the core data model and shared platform services, defining how domain objects and large-scale geospatial, imagery, inspection, and operational datasets are represented, connected, processed, governed, and exposed to applications, customers, and AI systems.
Key responsibilities include:
- Own the roadmap for shared platform services and data capabilities, working across product teams to understand needs, unblock high-value use cases, and drive consistency without constraining product innovation.
- Maintain clear understanding of the domain model, datasets, services, APIs, and platform dependencies; prioritize and make trade-offs legible to engineering, product, and leadership.
- Partner with engineering to build reliable, scalable APIs, services, pipelines, and interfaces that turn complex systems into simple platform capabilities.
- Define canonical entities, relationships, contracts, events, permissions, and semantics used across the data lifecycle.
- Improve how application teams discover, understand, and consume shared capabilities; define standards for data quality, lineage, governance, discoverability, documentation, reliability, and developer experience.
- Identify where shared capabilities should be standardized at the platform layer versus owned within individual products or verticals.
- Evaluate how AI and agent-based systems should interact with Zeitview's data and platform capabilities, including primitives required for reliability and scalability.
- Define success metrics for platform investments and measure adoption, reliability, reuse, and business impact.
This is a technical, high-leverage product role for someone who cares about building reliable, scalable, extensible systems. You'll work closely with product engineering, data engineering, data science, security, and product teams to turn fragmented systems and data into durable platform primitives. Note: this is not an infrastructure-only role, not a data engineering role, not a centralized gatekeeper role, and not a single-product role—your customers are application teams, engineers, data practitioners, AI systems, and ultimately customers who depend on platform capabilities.
REQUIREMENTS:
- 8+ years of professional experience, including 5+ years in product management
- Meaningful experience owning technical platform, data, infrastructure, developer, or AI-enabling products
- Proven track record building and shipping enterprise B2B SaaS products or platforms used by multiple teams or product surfaces
- Fluency with technical concepts including data modeling, schemas, APIs, event systems, distributed systems, data pipelines, warehouses, or analytics infrastructure
- Ability to hold your own in architecture and technical design discussions with senior engineers and data practitioners
- Experience making consequential build-vs-buy, abstraction, architecture, reliability, or scalability trade-offs
- Ability to translate complex technical capabilities into clear product requirements and platform interfaces that engineers can build from
- Demonstrated success delivering ambiguous, cross-functional programs where alignment and influence mattered more than organizational authority
- Fluency in quantitative reasoning and ability to define metrics demonstrating whether a platform investment is creating value
- Understanding of enterprise expectations around security, permissions, privacy, governance, compliance, and reliability
PREFERRED:
- Experience with geospatial data, imagery, remote sensing, computer vision, or asset inspection platforms
- Experience with high-volume or complex data processing systems
- Experience defining domain models or shared data architectures across multiple products
- Experience building platforms that support machine learning, generative AI, or agent-based systems
- Experience with data lineage, observability, governance, or master-data concepts
- Experience working with operational systems where digital workflows connect to physical-world activity
