Senior Full-Stack Engineer - Data Intelligence Applications (several headcounts)

Job description

At Statista , we’re all about facts and data, for we are the world's leading business data platform. By providing reliable and easy-to-use data as well as various data analytics products and services, we empower people worldwide to make fact-based decisions. Founded in Hamburg in 2007, we have quickly grown into a global company with offices in major cities such as London, New York, Berlin and Tokyo. And we still have a lot of plans. Our constant growth does not only prove our success, but also keeps creating new development and career opportunities for our employees. We value and celebrate our diverse culture. You are welcome here for who you are, no matter where you come from, what you look like, or whether you prefer bar graphs to pie charts. Your story matters – keep writing it as part of our team. Are you ready to join us? About the Role You will own and build the customer-facing layer of our platform: a portfolio of data intelligence products for hospitals, as well as the shared application framework beneath them. As the senior technical lead for this area, you will operate with high agency—designing how data is queried, cached, and visualized, building API layers over platform services, and working directly with domain experts to translate complex methodology into intuitive, high-impact products. Key Responsibilities End-to-End Product Ownership: Architect, build, and maintain data intelligence applications from database query contracts down to interactive UI components. Framework & API Layer: Build and own the shared application API layer, composing underlying platform services and published data models into stable contracts. Data-Access & Performance Strategy: Design robust caching, materialization, pagination, and streaming patterns to serve complex analytics from cloud data warehouses with low latency and optimized compute cost. Advanced Data Visualization: Build interactive, non-standard visualization interfaces (e.g., hospital performance benchmarking and peer comparison views) and establish reusable patterns for the broader portfolio. AI-Mediated Interfaces: Implement natural language and LLM-driven application surfaces—including retrieval workflows, tool integration, structured outputs, streaming, and evaluation of latency and cost. Technical Leadership: Shape application architecture, set engineering standards, drive testing discipline, and…