Senior DevOps Engineer

Spotlite turns satellite imagery into risk intelligence for critical infrastructure. Utility, energy and transport operators across Europe and the Americas use our products, GeoPulse (ground displacement monitoring with InSAR) and TreeGuard (vegetation encroachment along power lines), to monitor what they cannot inspect on foot.

As Senior DevOps Engineer, you’ll own our infrastructure end to end, from the cloud environment and deployments to our data pipelines and the visibility the team has over them. You’ll be the central accelerator for our Geospatial Data, Machine Learning, InSAR and Platform teams, helping us scale delivery for large contracts with less operational friction. You’ll work primarily with the Head of Data & R&D and the Director of Software Engineering. This is a hands-on DevOps role, not a software development role.

About the role

Spotlite turns satellite imagery into risk intelligence for critical infrastructure. Utility, energy and transport operators across Europe and the Americas use our products, GeoPulse (ground displacement monitoring with InSAR) and TreeGuard (vegetation encroachment along power lines), to monitor what they cannot inspect on foot.

As Senior DevOps Engineer, you’ll own our infrastructure end to end, from the cloud environment and deployments to our data pipelines and the visibility the team has over them. You’ll be the central accelerator for our Geospatial Data, Machine Learning, InSAR and Platform teams, helping us scale delivery for large contracts with less operational friction. You’ll work primarily with the Head of Data & R&D and the Director of Software Engineering. This is a hands-on DevOps role, not a software development role.

What you’ll own

• Infrastructure leadership. The end-to-end DevOps lifecycle, working independently to identify bottlenecks and design scalable solutions without requiring micromanagement.
• Cross-team orchestration. Partnering directly with the InSAR, Machine Learning, Geospatial Data and Platform teams to understand their compute, storage and deployment requirements, and providing the infrastructure they need to succeed.
• Azure cloud architecture. Architecting, deploying and maintaining highly available, secure and cost-effective Microsoft Azure environments (Blob Storage, etc.) tailored for heavy geospatial workloads.
• Geospatial API scaling. Deploying and optimizing geospatial-oriented APIs to handle large-scale spatial queries and data retrieval.
• Process automation and CI/CD. Designing, implementing and maintaining continuous integration and deployment pipelines in GitHub Actions and Azure DevOps Pipelines, to automate software delivery and ML model deployment (MLOps).
• Data workflow orchestration. Implementing and managing workflow orchestration platforms for complex, heavily dependent scientific data processing pipelines.
• Security (DevSecOps). Building security into the delivery lifecycle: secrets and identity management, dependency and container scanning in the pipelines, and secure configuration of our Azure environments.
• FinOps. Monitoring, reporting and optimizing cloud spend across environments, and flagging cost risks early, especially from heavy data processing workloads.
• AI-orchestrated pipelines. Operating and evolving pipelines that rely on strong artificial intelligence orchestration. We cover the details during onboarding.

What we’re looking for

• 5+ years of experience in DevOps, Cloud Engineering or Site Reliability Engineering (SRE).
• Deep, hands-on experience with Microsoft Azure services relevant to big data and containerization.
• Extensive experience building automation pipelines, with strong proficiency in GitHub Actions and Azure DevOps Pipelines.
• Proven experience with modern data orchestration platforms, specifically Dagster or Kestra.
• Proficiency in Infrastructure as Code with Bicep, and in containerization with Docker.
• Strong development skills with Python as your primary language for scripting, automation and tooling.
• Solid understanding of geospatial standards and APIs, and experience deploying and scaling these services.
• Familiarity with the challenges of moving and processing large-scale raster/vector data and Earth Observation imagery.
• Practical experience embedding security into CI/CD and cloud infrastructure (secrets management, identity and access, vulnerability scanning), and tracking and optimizing cloud costs.
• A demonstrated ability to thrive in a self-directed environment, taking initiatives from concept to production.
• Excellent technical communication, to translate the needs of highly specialized scientific teams into robust platform infrastructure.

Nice to have

• Experience supporting Machine Learning workflows (MLOps, model registries, GPU-accelerated compute provisioning).
• Experience with geospatial databases and open-source tooling (e.g., PostGIS, GDAL, Rasterio).