About this role
Everything happens somewhere - which is why spatial analytics is fundamental to companies trying to understand the “where” and the “why” of their business. CARTO is the leading Location Intelligence platform, empowering companies with scalable and cloud-native spatial analytics. CARTO helps organizations make better business decisions by empowering data analysts, business analysts, GIS professionals, and developers with faster, more flexible, and more secure spatial data analysis and visualization tools. Whether through optimizing network planning, assessing risk, identifying growth opportunities, or other use cases, companies benefit from turning their location data into business value. With an exceptionally diverse team of 150 people spread across the US and Europe, CARTO (backed by Insight Partners, Accel Partners, Salesforce Ventures, and Earlybird Ventures, among others) is changing the way companies analyze location data - making it simple to do this straight out of modern, cloud data warehouses. Redefining its category, the company has grown rapidly in recent years providing a compelling alternative to legacy GIS software. To continue this growth, the engineering team is looking for an enthusiastic and ambitious Software Engineer in Test to work on the end-to-end quality of our platform. Location: The position is based in Spain. We do have offices in Madrid and Seville, but we're open to candidates based anywhere in Spain. Build AI tooling that turns requirements, specs and tickets into test plans and executable test cases Use AI agents to write, extend and repair automated tests — and own the judgment call on what they produce: the plausible-but-wrong test, the assertion that can never fail, the coverage that only looks like coverage. This is the core skill of the job. Automate execution: suites that run on every change, stay fast, don't flake, and fail in a way someone can diagnose without re-running them locally Work with Development and Product Management as their quality partner — help engineers test their own features properly, review test coverage on PRs, stop regressions before production Convert manual test cases into automated ones, and keep an honest inventory of what's left and why Run the manual testing that still needs a human: exploratory passes on new features, visual and cartographic correctness, complex data flows across data warehouses Build the test data the automation depends on — datasets, warehouse connections, maps consuming multiple data sources Get deep enough in spatial SQL to validate our analysis output yourself