Lead Data Engineer @Panora (backed by Hexa)

Panora is building a suite of AI-powered agents for insurance brokers - a highly regulated and operationally complex industry.

Our ambition is to become the AI Operating System for brokers in Europe: automating high-friction workflows while improving compliance, advisory quality, and client relationships.

The product is already live with brokerage firms across France and Belgium, with AI assistants in production (quotation automation, contract comparison, coverage analysis, compliance checks, document generation…).

Panora enables brokers to save hours every week, reduce analysis time by up to 70%, and focus on what truly matters: advising clients .

Founded by Diane du Paty (ex-VC, operator) and Fabian Langlet (repeat founder, AI product engineer), Panora is backed by Hexa (Aircall, Spendesk, Front).

We are entering a key phase of scaling and industrializing our AI platform.

At Panora, AI is not a feature - it is the core product layer, deployed in real-world conditions with strong constraints on reliability, traceability, and compliance.

Role

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.

Responsibilities:

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

Our Technology Stack

Built for an AI-first, production-grade product:

• 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.