Senior Software Engineer, Quality Platform

Puzzle.io•San Francisco, CA

1d•$173,000 - $183,000•Remote

About The Position

Puzzle is rebuilding the accounting stack from the ground up, moving away from outdated tools that rely on manual entry and slow workflows. Our core is a real-time financial engine that processes high volumes of data from modern fintech tools into clean, reliable financials. We are building workflows, automation, and insights to help accounting professionals work faster and more accurately. We operate at a fast pace, ship frequently, and prioritize correctness. We are seeking a senior engineer to enhance how we validate product quality across our engineering organization. This role is broader than traditional QA Automation Engineer, SDET, or Quality Engineer. We are looking for an engineer who can build systems, frameworks, workflows, and feedback loops to improve product safety and ease of change. Our engineering team already embraces quality as part of the development process, with engineers writing various types of tests and investing in tooling for early and confident feature validation. As our product expands, we aim to strengthen our validation across modern UI, GraphQL API, and partner-facing REST API. This role will focus on building more comprehensive, reliable, and scalable validation across these layers, addressing questions about feature correctness, workflow evolution, API behavior validation, end-to-end test efficiency, and the thoughtful use of AI in testing.

Requirements

7+ years of professional experience in software engineering, quality engineering, SDET, infrastructure, reliability, or a closely related technical role.
Strong software engineering fundamentals and the ability to write production-quality code, not just test scripts.
Experience designing and maintaining automated test frameworks for complex SaaS applications.
Hands-on experience with modern browser automation tools such as Playwright, Cypress, or Selenium.
Strong API testing experience, ideally with GraphQL and REST.
Experience testing complex business workflows involving state, permissions, data integrity, async jobs, integrations, and third-party systems.
Comfort working in CI/CD environments and understanding how tests should behave in pull requests, staging, release pipelines, and production-like environments.
Pragmatism about testing strategy. You know every bug does not require an end-to-end test, and every test does not belong in the UI.
Curiosity about AI-assisted engineering and testing, balanced with strong engineering judgment.
Clear communication around risk, tradeoffs, gaps, and why a certain kind of validation matters.

Nice To Haves

Experience in fintech, accounting, payroll, billing, tax, ERP, financial reporting, or other correctness-sensitive domains.
Experience testing multi-tenant SaaS applications.
Experience with schema-driven testing, contract testing, property-based testing, generated test cases, or model-based testing.
Experience with observability, synthetic checks, production validation, or SRE-style reliability practices.
Experience testing authorization, roles/permissions, audit trails, financial calculations, ledgers, reconciliation flows, or data pipelines.
Experience using LLMs or AI coding agents to generate tests, analyze failures, improve developer workflows, or build internal quality tools.
Experience with partner APIs, public APIs, SDKs, or integration ecosystems.

Responsibilities

Help define and build our quality engineering strategy across UI, API, data, and partner-facing surfaces.
Improve and extend end-to-end test coverage for high-value workflows, with an emphasis on maintainability, signal quality, and developer confidence.
Build deeper API-level validation for our GraphQL server and partner-facing REST surfaces, including contract testing, schema validation, regression coverage, and test data strategy.
Work with product engineers to choose the right level of validation for each problem: unit, integration, API, contract, end-to-end, synthetic monitoring, observability, or exploratory testing when appropriate.
Use AI and LLM-based tools thoughtfully to accelerate quality work, such as generating test cases, identifying coverage gaps, creating fixtures, analyzing failures, or building internal testing utilities.
Know when not to use AI. A script, deterministic check, schema validator, or focused test runner is often the better answer.
Help define what “done” means for features from a quality perspective, without becoming a bottleneck.
Mentor engineers on writing testable software, designing stable interfaces, and creating systems that are easier to validate.
Collaborate with engineering, product, customer success, and support to turn real customer workflows and production learnings into better automated coverage.