Job Description Climate Tech Startup - Not for Profit - Senior Python Engineer - £500-600/day (outside IR35) - RemoteContract Overview:Senior Python EngineerNot-For-Profit ClimateTech Startup6 month contract, likely to extend by another 3 monthsStart ASAP£500-600/dayOutside IR35Fully remoteInterview process: tech test (2 hours) and interview (1.5 hours)Company Overview:We are an open data climate startup (with secured core funding), on a mission to map the global policy landscape, harnessing machine learning to create the evidence base for informed decision-making. Our work helps governments, the private sector, researchers and civil society to advance effective climate policies rapidly, replicate successful approaches and avoid failed ones, enhance accountability and promote data democratisation.Mission / Purpose:We are on a mission to map and analyse the climate policy landscape globally and drive the transition to a low carbon, resilient world.Tech Stack:Python, FastAPI, PostgreSQL, ElasticSearch, SQL Alchemy, AWS, DockerScope of work:First 3 months:Developing the backend (database and services) for our Alpha version of the productDeploying our alpha release into our AWS cloud environmentSecond 3 months:Improving the scalability of our alpha product to be ready to release more widely as part of our beta releasePutting in place enhancements to be released in our beta release following feedback from early adopters and partnersSkills needed:Experienced using Python with one of the modern frameworks (Django, Flask, FastAPI, Tornado etc) to build REST API’sExperienced in system architecture. Comfortable building a greenfield product and making critical technical decisions.Experienced in AWS and functions like EC2, S3, Lambdas, auto-scaling, security. Able to select and set up the right configuration for what we need alongside DevOps tooling (Docker, ECS)Strong database knowledge, SQL and PostgreSQLSolid understanding of software engineering fundamentals, version control (Git), shell scripting, OOP, unit and integration testing.Nice to haves:Working on an AI driven product. Used to working closely with data scientist or machine learning engineers, understanding some of the concepts and terminology.Data engineering skills. Any experience of building scalable data pipelines for ingestion and processing of large unstructured data sources, incorporating machine learning models, using tools like Airflow or Dagster.