Lead Platform Configuration Analyst

Lead Platform Configuration Analyst

Job type:
Full Time
· Department:
Operations
· Work type:
Hybrid

Vancouver, British Columbia, Canada

Cover Genius is a Series E insurtech that protects the global customers of the world’s largest digital companies including Booking Holdings, owner of Priceline, Kayak and , Intuit, Uber, Hopper, Ryanair, Turkish Airlines, Descartes Ship Rush, Zip and Seat Geek. We’re also available at Amazon, Flipkart, eBay, Wayfair and SE Asia’s largest company, Shopee. Our partners integrate with XCover, our award‑winning insurance distribution platform, to embed protection for millions of customers worldwide each year.

Our team and products have been recognized with dozens of awards including by the Financial Times which ranked Cover Genius as the #1 fastest‑growing company in APAC in 2020. Our diverse team across 20+ countries and many language groups commits itself to diverse cultural programs, in particular “CG Gives” which makes social entrepreneurs out of us all and funds development initiatives in global communities.

Bold, Authentic, Purposeful and Inspired

Perfect, Traditional, Complacent or Cautious

As a Policy Configuration Analyst, you own the full lifecycle of our AI‑augmented policy delivery model for configuring hundreds of insurance products across global partners. Your mission is to move beyond manual processes by executing an AI‑first configuration strategy. This includes gathering requirements, managing the policy build portfolio, directing the Policy Agent with structured inputs, and acting as a fluent Python practitioner to supervise, validate, and correct the agent’s output.

You will also develop comprehensive automated testing, build custom agent skills to automate repeating build patterns for shared tooling, own post‑launch resolution, and ensure accurate documentation.

• Requirements & Discovery: Own the requirements gathering process for every policy build. You know which stakeholders to engage, which questions surface the information you actually need, and how to structure what you learn into clear requirements. You work with technical and GTM teams; resolving ambiguity early, chasing down what is missing, and documenting requirements in a form that needs no follow‑up.
• Portfolio Ownership: Manage a concurrent portfolio of policy builds across sprint cycles across the team. Maintain a clean backlog with delivery estimates that hold, and give transparent status updates without being chased.
• Policy Configuration: Translate your requirements into structured inputs that the Policy Agent can build from. You are responsible for directing the agent clearly enough that it produces correct configuration output, and for validating the Python it writes. The Policy Agent builds in Python and you supervise that output as a fluent Python practitioner. You can read what the agent produces, identify where it has gone wrong, and correct it.
  
  You own the result, not the keystrokes.
• Testing: Build and execute comprehensive test plans for all policy configurations prior to production deployment. Build and maintain automated test suites using our internal tooling across multiple demographic and scenario variations.
• Agent Ecosystem Development: Build custom skills and workflows within our existing agent ecosystem to automate repeating build patterns. Identify opportunities to push more of the pipeline to automated execution and contribute every improvement back to shared tooling. When you solve a build problem, you close it for everyone.
• Post‑Launch Ownership: Your responsibility doesn't end at go‑live. You own the resolution of post‑launch bugs and change requests — triaging issues, coordinating fixes, and seeing them through to closure. Partners and internal teams will come to you when something isn't working, and you are the person who gets it resolved.
• Documentation: Keep product and configuration documentation accurate and current across the full build lifecycle. Good documentation is not a finishing task — it is how the next build starts faster and how institutional knowledge stays in the team rather…