About BigGeo
BigGeo is the Spatial Cloud. We help companies manage and access the world’s spatial data. Any size, any slice, any insight. Delivered in seconds.
We’re building something that hasn’t existed before: a new layer of the internet where the “where” and “when” behind every decision is instantly clear, programmable, and actionable. Our platform removes the complexity that has kept spatial data locked in silos for decades, and replaces it with speed, precision, and control.
We’re a Calgary-based company, early and moving fast, with real customers, real infrastructure, and a clear point of view on where the world is going.
Why BigGeo Exits and Why People Build Here
Most companies are spatially blind. They know what their data says, but not where or when things actually happen. That gap costs real money, creates real risk, and limits what AI can actually do in the physical world.BigGeo exists to close that gap.We’re not building another tool. We’re building the rails that connect the planet’s moving data to the systems that run the world. That’s a big problem, and it takes people who care about doing things right, not just fast.
People build here because:
- The problem is real and the category is open. We’re not competing for the middle of an existing market. We’re defining a new one. Your work shapes what the category becomes.
- Your fingerprints are on the architecture. We’re at the stage where the decisions you make today become the foundation tomorrow. What you ship matters.
- We run on clarity, not politics. We move with purpose. No bureaucratic drag, just a team that agrees on the mission and gets to work.
- You’ll grow fast because the problems are hard. Spatial data at scale is a genuinely difficult domain. If you want to be stretched, you’ll be stretched.
- We’re building for longevity. We’re not chasing hype cycles. We’re building infrastructure, the kind that compounds in value over time and earns the trust of the companies that depend on it.
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.
Key Responsibilities
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
Success Metrics
First 30 days:
Full onboarding to the Spatial Cloud platform, the DGGS architecture, and the active engineering workstreams. Clear picture of the current referencing layer design, its constraints, and where the biggest architectural decisions still need to be made.
First 60 days:
Driving at least one meaningful architectural decision on the DGGS referencing layer with full accountability for the tradeoffs. Contributing to design reviews and technical documentation with clear written reasoning. Established working relationships across platform, data, and spatial compute teams.
First 90 days and beyond:
Recognized as the primary owner of the DGGS referencing architecture end to end. The referencing layer is more composable, more performant, or more interoperable as a direct result of your decisions. Actively shaping the technical direction of the Spatial Cloud platform and contributing to the engineering bar as the team grows.