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 Exists 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
BigGeo is hiring a Platform Engineer to build and operate the core infrastructure that powers the Spatial Cloud. This is a high-ownership role for someone who wants their engineering decisions to be felt in production, by real customers, on real workloads, every single day.
You will design, implement, and maintain the distributed systems that let spatial data be ingested, processed, and delivered reliably at scale. Your work ensures the Spatial Cloud operates as durable, high-performance infrastructure that teams, applications, and AI systems can trust to run the decisions that matter.
You will partner closely with engineering, data, and product to keep the platform reliable, scalable, and easy to build on as datasets grow, workloads intensify, and new product capabilities ship. Platform Engineers are the people who turn BigGeo’s architecture into production-grade infrastructure.
This is not a maintenance role. This is a build role. You will own systems end to end, ship meaningful changes quickly, and shape the technical foundation that the rest of the company builds on top of.
Key Responsibilities
Platform Infrastructure
- Design and implement scalable, distributed infrastructure that powers the Spatial Cloud across ingestion, storage, compute, and delivery.
- Build and operate distributed services that manage large spatial datasets and high-volume compute workloads.
- Keep platform systems performant under real production load, not just benchmark load.
- Contribute to architecture decisions that shape how the platform scales over the next three to five years.
Reliability and Operations
- Implement monitoring, logging, tracing, and observability systems that give engineers real visibility into the platform.
- Participate in on-call rotation, incident response, and post-incident reviews that turn outages into permanent fixes.
- Maintain high availability and operational stability across services that customers rely on.
- Define and uphold SLOs, error budgets, and reliability practices that scale with the team.
Developer Platform
- Build internal tools, libraries, and platform services that make every engineer at BigGeo faster and safer.
- Support the APIs and infrastructure that product and data teams depend on daily.
- Design platform primitives that are easy to operate, easy to extend, and hard to misuse.
Performance Optimization
- Profile and improve the performance of distributed services, data pipelines, and spatial compute paths.
- Identify bottlenecks across the stack: network, compute, storage, and serialization layers.
- Ship changes that measurably improve throughput, latency, and cost-to-serve as datasets and usage grow.
Infrastructure Automation
- Implement infrastructure-as-code, automated deployment pipelines, and reproducible environments.
- Maintain CI/CD systems that enable rapid iteration and safe, reversible releases.
- Reduce manual toil through automation so the team’s time goes into building, not babysitting.
Cross-Team Collaboration
- Partner with data engineers, product engineers, and spatial compute teams on the systems that span their domains.
- Contribute to architectural discussions and platform design reviews with clear, written reasoning.
- Help ensure the Spatial Cloud platform supports current product commitments and future product ambition.
What You Bring
Required:
- Bachelor’s degree in Computer Science, Software Engineering, or a related field.
- 3 to 7 years of professional experience in platform engineering, infrastructure engineering, or backend systems.
- Demonstrated experience building or operating distributed systems in production, not just in prototypes.
- Strong working knowledge of cloud infrastructure (AWS, GCP, or equivalent) and containerized environments (Docker, Kubernetes, or similar).
- Strong programming skills in one or more backend languages commonly used for systems work, such as Go, Rust, Java, C++, or Python.
- Track record of building reliable systems with proper monitoring, alerting, and observability practices.
- Hands-on experience with CI/CD and infrastructure-as-code workflows (Terraform, Pulumi, Helm, or similar).
- Ability to reason clearly about trade-offs in a distributed environment: consistency, latency, cost, and operability.
- Clear written and verbal communication. You can explain an architectural decision to a teammate and to a skeptical stakeholder.
Nice to Have:
- Experience working with geospatial or spatial data systems (PostGIS, H3, S2, GeoParquet, tile servers, or similar).
- Experience with high-performance data pipelines or real-time compute systems (streaming, columnar stores, vectorized execution).
- Familiarity with distributed data processing frameworks such as Spark, Flink, Ray, or equivalents.
- Experience supporting developer platforms, internal developer portals, or paved-road tooling.
- Prior experience in early-stage or high-growth engineering environments where scope is broad and priorities move.
- Exposure to ML or AI workloads running on shared infrastructure.
Advanced AI Skills
BigGeo is an AI-enabled engineering organization. Every Platform Engineer is expected to use modern AI tools to move faster, think more clearly, and build higher-quality systems. As a Platform Engineer, you will be expected to:
- Use AI coding assistants such as Claude Code, Cursor, or Copilot to accelerate implementation, refactoring, and test coverage on real production systems.
- Use AI to accelerate debugging and system analysis: reading logs, tracing incidents, diffing configurations, and exploring unfamiliar services.
- Use AI to draft and pressure-test technical design docs, runbooks, and post-incident writeups before review.
- Build internal automations and agentic workflows that reduce manual operational work across the platform team.
- Evaluate AI output critically. Treat it as a fast, capable collaborator whose work still needs engineering judgment before it ships.
- Share patterns and workflows with the team. If you find a faster way, codify it so everyone benefits.
Success Measures
First 30 days:
You have a working mental model of the Spatial Cloud platform, its services, and its data flows. You have shipped at least one meaningful change to production through the full CI/CD and on-call flow. You have established working relationships across engineering, data, and product.
First 60 days:
You own at least one platform surface area outright, with visible improvements to reliability, performance, or developer experience. You are contributing to architecture and design reviews with clear written reasoning. You have identified at least one area of toil or fragility and have a credible plan to fix it.
First 90 days and beyond:
You are a trusted operator during incidents and a trusted reviewer on platform changes. Your work measurably improves the reliability, throughput, or cost profile of core Spatial Cloud infrastructure. You are shaping what the platform looks like twelve months from now, not just keeping today’s version running.
