Senior GIS Software Engineer, Climate X

• Strong backend engineering skills in Python, with experience building scalable, performant data processing systems and RESTful APIs; ideally following a TDD approach
• Hands‑on experience with geospatial data formats, with an emphasis on raster data, and relevant tooling (e.g., GDAL, QGIS, Rasterio, xarray, STAC)
• Ability to collaborate effectively with multidisciplinary teams (ML, Science, Engineering)
• Problem‑solving mindset with a passion for tackling large‑scale data and performance challenges

Desirable

• You’ll be at the front of a nascent industry, working as part of a fantastic and diverse team, doing things that you can be proud of. We’re excited to have the opportunity to speak with you during this process
• We’re helping the world become more resilient to the impacts of physical climate risks (including floods, fires, storms and more). In doing so, we help drive positive global impact aligned to many of the UN’s Sustainable Development Goals (SDG’s)
• Our team builds cutting-edge, peer-reviewed science (incorporating climate projections, remote sensing data) and translates that into financial impacts (to the value of assets or business disruption linked to failure of critical infrastructure) that our customers in the financial services industry use to identify, manage and mitigate those risks
• Climate X’s customers include the world’s largest banks, asset managers and insurance companies including CBRE, Standard Chartered, Virgin Money and Federated Hermes, as well as a partnership ecosystem that includes Deloitte, Capgemini and AWS. Combined, they manage over $6.5 trillion of assets
• Customers choose us thanks to our ecosystem of products that help solve real human problems and drive tangible business benefits. They love our customer-centric mindset, as well as our pace of innovation in the market
• As a Senior Geospatial Software Engineer, you will join our Geo Pod and work at the intersection of software engineering, geospatial data, and machine learning
• This is a unique opportunity to contribute to cutting‑edge projects tackling global climate risk challenges
• AWS POC Project (IRCAI): Collaborate with our Machine Learning and Science teams on the AWS–IRCAI Climate Fellowship initiative, building innovative tools for climate risk modelling
• Raster Scaling Challenge: Lead the development of a next‑generation solution to manage and process rapidly growing, high‑resolution raster datasets at scale
• Scientific Software Engineering: Partner with our climate scientists to improve the scalability, performance, and reliability of scientific models through expert‑level Python coding
• Design, build, and optimise backend systems for large‑scale geospatial and climate data processing
• Identify performance bottlenecks in raster data workflows and propose innovative, future‑proof solutions
• Collaborate with ML engineers and scientists to integrate and scale scientific models in production
• Build and maintain robust data pipelines in a cloud environment (AWS)
• Contribute to proof‑of‑concept and production projects with a focus on quality, scalability, and maintainability