Lead Platform Configuration Analyst

Cover Genius•Vancouver, BC

About The Position

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

5 years in a technical analyst, platform engineering, or configuration role.
You can write Python, read an API spec, interpret a JSON response, and debug a broken booking flow without needing to escalate.
You have hands-on experience building agents, automated workflows, or custom tools.
You have hands-on experience with APIs (Postman, cURL), test automation frameworks, and the instinct to deploy AI Agents or automate anything you do more than twice.
You possess the rare ability to meet non-engineering stakeholders where they are, create a desire for progress, and then translate their needs into technical, engineering-ready specifications.
You are strong numerically. You can read pricing requirements, interpret actuarial inputs, and validate financial structures without switching off - you enjoy solving interesting puzzles!

Nice To Haves

You know what AI models dropped this week and have already tried them. You don't wait to be onboarded to the Platform Agent — you read the documentation before the meeting.
You encounter a blocker and you resolve it. You bring solutions to standup, not problems. You prefer a rough first build over a perfect brief.
You hate doing the same thing twice. When you build a new policy configuration, you are already thinking about how to template it for the next ten.
Ambiguity does not stall you. You identify what you need, find it, and move. You escalate when genuinely blocked — not to offload thinking.
You build trusted relationships with Insurance, Actuarial, Data, and Partnerships teams. But you do not need to be managed. You manage yourself.

Responsibilities

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.
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.
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.
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.
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.
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.
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 than in someone's head.