Senior Backend Engineer, Growth Platform

Lemonade transformed insurance by replacing legacy processes with automation and machine learning. On the Growth Platform team, you will architect and build a brand-new growth engine: an event-driven platform that ingests customer signals from across our ecosystem to power intelligent decision-making across all customer touchpoints.

You'll have full architectural ownership of the services and data pipelines that evaluate user context, trigger outreach, and directly influence company growth.

In this role you'll

• Architect, build, and scale backend services that ingest and unify customer data from multiple sources into actionable signals for personalization, recommendations, and lifecycle journeys.
• Design the decision engines and business logic that orchestrate outreach across communication channels, integrating experimentation frameworks and machine learning models.
• Partner with product, data, and machine learning colleagues to turn customer insight into features people actually use.
• Own your services end to end, from code quality and performance to reliability after launch.
• Spot gaps in how we serve customers and drive the fix yourself.
• Drive engineering excellence - Mentor engineers and help shape the engineering roadmap of the team as the team grows.

What you'll need

• 7+ years building production backend systems that run at scale.
• A track record of taking a problem from definition to a shipped, running solution with little day-to-day direction.
• Hands-on experience with at least one modern backend language, plus proof you pick up new stacks and tools quickly.
• A product mindset: you can explain why your work matters to customers and to the business.
• A BSc in Computer Science, Mathematics, or equivalent experience.
• Ready to work in an office environment most days of the week
• Bonus points for
• TypeScript and NestJS, customer lifecycle or CRM data, recommendation systems or experimentation platforms, or open-source contributions.
• Experience with customer lifecycle systems, customer data platforms (CDP), or dynamic experimentation frameworks.
• Experience integrating machine learning model outputs or predictive scores into backend workflows.
• Active contributions to open-source software.