• Design, build, and maintain API integrations between various SaaS platforms • Leverage middleware platforms to orchestrate multi-step automation workflows • Develop and maintain integrations using RESTful APIs, webhooks, OAuth 2.0 • Ensure all integrations meet compliance requirements • Document integration architectures and data flows • Proactively identify inefficiencies and propose improvements • Manage multiple integration projects concurrently

• Development of scalable backend services and APIs • Integration of external services and partner interfaces • Database design and optimization • Implementation of business logic for premium calculation and payouts • Performance optimization and ensuring scalability • Use of AI coding agents to enable efficient development • Create and maintain technical documentation

• Development of new features from frontend to backend • Implementation of user interfaces according to design specifications • Implement API integrations and database operations • Translate user stories into working code • Use AI coding agents for rapid prototyping and implementation • Bug fixing and continuous improvement of existing features • Participate in code reviews and pair programming

• Design, develop, validate, deploy and evolve analytical and predictive models that deliver measurable business impact. • Structure and lead end-to-end data science initiatives. • Understand business problems and translate them into analytical hypotheses, statistical models and machine learning solutions. • Develop, train, validate and monitor predictive, classification, prescriptive and segmentation models. • Work with large volumes of structured and unstructured data. • Build analytical pipelines and participate in the industrialization/operationalization of models in production. • Define model performance metrics and monitor drift, accuracy, stability and business impact. • Perform data exploration, feature engineering, variable selection and statistical testing. • Support analytical data architecture together with Data, Engineering and BI teams. • Ensure best practices for versioning, documentation, reproducibility and model governance. • Communicate analytical results in an executive, decision-oriented manner. • Act as a technical reference for more junior analysts and data scientists. • Participate in defining the data roadmap, use cases and prioritization of AI and Analytics initiatives.