Software Engineer In Test

Clara is the leading platform in Latin America for companies to execute and manage every payment operation. More than 50,000 leading and fast-growing businesses run on our corporate cards, bill pay, financing and B2B payments, and in 2025 the Financial Times named Clara the region’s fastest-growing company. We’re passionate about helping every company we work with become more effective—and, through that, raising the region’s competitiveness. We’re ~400 people, backed by Notable Capital, Coatue, Kaszek, monashees, DST Global, ICONIQ, General Catalyst and Goldman Sachs, among others.

We are looking for a Fullstack/Backend Engineer focused on QA Automation — a software engineer who builds the infrastructure, frameworks, APIs, and tooling that make automated quality possible at Clara. This is an engineering craft role (closer to SDET / SET / platform engineer than classic manual QA): you write production-grade code that enables pods and QA to generate, run, and trust automated tests.

The Role

You build and maintain the systems behind Clara's QA platform: Playwright-based frameworks, services and APIs that support test execution and reporting, CI wiring, and internal tools (e.g. test case / run persistence on Clara's React/Next stack). You collaborate with the QA Automation Lead, Pod Leads, and product engineers so automation is reusable, agent-ready, and owned as real software — not a pile of brittle scripts.

What You Own

• Automation infrastructure — Design and implement shared frameworks and libraries for web/API automation (Playwright preferred), with stable selectors, modular architecture, and clear extension points for pods.
• Tooling & services — Build backend APIs and fullstack components that store test cases, record run results, expose reports, and integrate with delivery workflows (e.g. Linear webhooks, Slack notifications, GitHub Actions).
• Enablement for AI-assisted QA — Structure frameworks and contracts so agents and CLIs can generate and execute specs reliably (ticket → matrix → runnable tests → evidence).
• CI/CD for quality — Implement and harden pipelines for smoke and regression suites; reduce flakiness; make failures diagnosable.
• Engineering quality of test code — Treat automation as a first-class codebase: reviews, tests for helpers, versioning, documentation, and refactoring for maintainability.
• Partnership — Work with QA engineers on adoption and with product engineers on testability (APIs, data-testid / ARIA, environments, test data hooks).