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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Founding Data Engineer - **Company:** Panora, Ai - **Location:** Inconnu, France - **Contract:** Permanent contract - **Skills:** Microsoft Windows, Artificial Intelligence, Amazon Web Services, Information Engineering, Data Infrastructure, Data Systems, Monitoring of Systems, Python (Programming Language), Machine Learning, MongoDB, Systems Integration, TypeScript, Unstructured Data, Large Language Models, Backend, Code Restructuring, Data Pipelines, Serverless Computing, Service Stack - **Published:** September 5, 2026 - **Apply:** https://startup.jobs/lead-data-engineer-panora-backed-by-hexa-hexa-8611974 ## About the Role We're looking for a builder who ships, enjoys solving difficult problems, and thrives in a small, highly collaborative team. * Experience building, operating and improving production systems in a startup or product-driven environment * Experience working closely within an engineering team and contributing to a shared codebase * Strong Python and backend engineering skills * Experience with data-intensive products, AI systems, LLM applications or applied machine learning * Comfortable working with messy, incomplete and unstructured real-world data * Able to quickly understand, improve and scale existing systems-not just build greenfield projects * High standards for reliability, data quality, maintainability and customer impact in production * Comfortable taking ownership, moving quickly and making progress in ambiguous environments * Strong product mindset: you care about solving meaningful customer problems, not just shipping technical features Bonus: * Experience with evaluation systems, feedback loops or AI observability * Experience in fintech, insurance or other regulated environments ## Description We're looking for a Founding Data Engineer to help build and scale the systems powering Panora's AI products. Your work will sit at the intersection of: * AI systems: LLMs, agentic workflows, evaluation pipelines, tracing and observability * Data engineering: ingestion, normalization, enrichment, extraction and document-processing pipelines * Internal datasets: building high-quality, structured insurance datasets from policies, quotes, underwriting questionnaires and business rules * Product & backend engineering: designing robust APIs, data models and scalable services used directly in production * Insurance expertise: translating real-world insurance workflows, contract language and decision rules into usable data systems As the third engineering hire, you'll work directly with Fabian (CTO & Co-founder) and Jeremy (Founding Software Engineer), with strong ownership and direct impact on both product and technical direction. What to Expect * Join an existing, large-scale codebase and quickly develop a deep understanding of the systems powering Panora. * Improve, refactor and scale critical parts of the platform while contributing new capabilities where they create the most impact. * You'll work with complex workflows, unstructured data and real production constraints. * You'll own problems end-to-end, from AI systems and data pipelines to customer impact. * Success is measured by product impact, reliability and customer outcomes., Build & Improve AI Products * Design, ship and improve AI-powered workflows used daily by insurance brokers * Build evaluation, feedback and monitoring systems to continuously improve performance * Turn complex insurance workflows into reliable AI-powered products Build the Data Foundations * Build and maintain data pipelines powering our products * Process and structure unstructured data (contracts, emails, insurer documents) * Improve the quality, reliability and observability of our systems Own & Scale Systems * Own systems end-to-end: from design and implementation to deployment and monitoring * Contribute to architecture and key technical decisions * Help define how AI, data and engineering scale at Panora, * Languages: Python for AI, data and automation; TypeScript for backend services and product integrations * AI systems: LLM-powered agents, structured extraction, evaluation frameworks, tracing, observability and feedback loops * Infrastructure: AWS, with a serverless and managed-services approach designed for reliability and scale * Data: MongoDB, document-processing pipelines, structured extraction workflows, internal datasets and evaluation datasets * Integrations: Microsoft 365, CRM and ERP tools, insurer extranets and other systems used daily by insurance brokers * Engineering environment: a large, evolving production codebase: where improving, refactoring and scaling existing systems is as important as building new ones What matters most is your ability to design robust systems, work with real-world constraints, and learn fast. ## Related Videos - [Developing the Backend with Stefan Lingler, CTO at Shpock](https://www.wearedevelopers.com/videos/100360-developing-the-backend-with-stefan-lingler-cto-at-shpock) - [40 Minutes to Build a Serverless COVID-19 REST and GraphQL APIs](https://www.wearedevelopers.com/videos/208-40-minutes-to-build-a-serverless-covid-19-rest-and-graphql-apis) - [Do TypeScript without TypeScript](https://www.wearedevelopers.com/videos/327-do-typescript-without-typescript) - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Nest.js - TypeScript in the backend can also be clean](https://www.wearedevelopers.com/videos/1033-nest-js-typescript-in-the-backend-can-also-be-clean) - [Meet Your New BFF: Backend to Frontend without the Duct Tape](https://www.wearedevelopers.com/videos/682-meet-your-new-bff-backend-to-frontend-without-the-duct-tape) ## Related Articles - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [Dev Digest 137 - AI'm not sure about this](https://www.wearedevelopers.com/magazine/485-dev-digest-137-ai-m-not-sure-about-this) - [Fully Remote Software Engineer Jobs](https://www.wearedevelopers.com/magazine/447-fully-remote-software-engineer-jobs) - [Dev Digest 120 - Apple and peers](https://www.wearedevelopers.com/magazine/455-dev-digest-120-apple-and-peers)