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Salary: USD 215,000 - 225,000 / annual
Zeitview is the leading intelligent aerial imaging company for high-value infrastructure, providing actionable, real-time insights to enterprises in solar, wind, insurance, construction, real estate, and critical infrastructure across 70+ countries.
In this role, you will own the platform that powers every Zeitview product. You are responsible for the core data model and shared platform services that enable data capture, processing, analysis, and delivery across Zeitview's products and verticals. You will define 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. This is a technical, high-leverage product role for someone who cares about building reliable, scalable, extensible systems.
Key responsibilities include:
- Own the roadmap for shared platform services and data capabilities
- Work across product teams to understand platform 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
- 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
- Evaluate how AI and agent-based systems should interact with Zeitview's data and platform capabilities
- Define success metrics for platform investments and measure adoption, reliability, reuse, and business impact
You will 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
- Track record of 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
- Track record of delivering ambiguous, cross-functional programs where alignment and influence mattered more than organizational authority
- Fluency in quantitative reasoning and ability to define metrics that demonstrate 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
