Spatial Software Architect

We help companies manage and access the world's spatial data. Any size, any slice, any insight. Delivered in seconds.

The Spatial Cloud provides a unified architecture that enables organizations to manage spatial data they own, access spatial data from external sources, and produce decision-ready intelligence in seconds.

By combining Unified Data, Real-Time Compute, and Governed Monetization, BigGeo transforms fragmented spatial datasets into a shared operational layer that powers real-world awareness across industries, infrastructure systems, and AI platforms.

Why BigGeo Exists and Why People Build Here

We start by being clear about what we're building and why it matters. BigGeo is building the Spatial Cloud, a foundational infrastructure for how spatial data is managed, accessed, and used in real time across systems, AI, and organizations.

Joining BigGeo means contributing to technology that is shaping a new category as it takes form. The BigGeo team is AI-enabled, cross-functional, and highly collaborative. We value ownership, clarity, and leveraging AI tools to build incredible momentum. The work moves quickly, expectations are high, and impact is visible because what we build is used in real-world environments.

At BigGeo, you will:

• Contribute to systems that influence how spatial data is managed and applied
• Leverage cutting edge AI tools so that your work shows up in real, high-impact use cases
• Collaborate with a multidisciplinary team solving complex, meaningful problems
• Work in an environment where autonomy is trusted, results matter, and AI tools are harnessed to boost creativity, output, and results

If you want to do work with real-world impact and help build foundational technology that changes how organizations understand and use spatial intelligence, BigGeo is the place to build that future.

The Role

The Spatial Cloud runs on a referencing architecture that most systems have never had to think about. BigGeo is building DGGS, Discrete Global Grid System, as a native platform primitive: a unified spatial index that makes any data, at any scale, instantly addressable and composable across systems. This role owns that architecture end to end. You will make the design decisions, navigate the tradeoffs, and build the infrastructure that makes DGGS a first-class citizen in a distributed cloud platform. If you have the instincts to reason about how a referencing layer becomes an infrastructure primitive, and the track record to build distributed spatial systems at scale, this is the hardest, most consequential problem in spatial computing right now.

What You\'ll Do

• Design and own the distributed architecture that makes DGGS a native referencing layer across the Spatial Cloud, including data models, query paths, and compute boundaries
• Drive system design decisions end to end with full accountability for architectural tradeoffs, not just participation in them
• Build and evolve the spatial indexing and referencing infrastructure that enables real-time, cross-scale data access at cloud scale
• Define integration patterns that allow external systems, AI agents, and organizational data sources to interoperate through a unified spatial layer
• Use AI-assisted development workflows, including Claude and Cursor, to accelerate design iteration, documentation, and system validation
• Contribute to the technical direction of Nexus, BigGeo\'s agentic spatial intelligence platform, ensuring the referencing foundation is built to support autonomous, real-time spatial decision systems
• Participate in technical interviews and architecture reviews to help raise and maintain the engineering bar as the team grows

What You Bring

Required:

• 8+ years of software engineering experience with a clear focus on distributed systems design and large-scale data infrastructure
• Proven track record of owning end-to-end system architecture decisions in production environments, not just contributing to design but driving it
• Deep understanding of spatial data structures, indexing strategies, and the tradeoffs involved in building geospatial systems at scale
• Ability to reason about how a referencing or indexing layer functions as a platform primitive, including its implications for query performance, data composability, and system interoperability
• Strong written communication and system documentation skills - you can turn complex architectural decisions into shared understanding across technical and non-technical stakeholders
• Demonstrated AI literacy and comfort using AI tools such as Claude and Cursor to accelerate engineering workflows, design iteration, and technical documentation

Nice to Have:

• Familiarity with DGGS frameworks - this is a strong preference, not a hard requirement; the architectural instincts to learn DGGS quickly matter more than prior exposure
• Experience building geospatial or earth observation platforms at cloud scale
• Exposure to agentic AI systems, MCP server architecture, or real-time spatial compute
• Background in defence, energy, or critical infrastructure domains where persistent spatial awareness or real-time deconfliction is a system requirement