QA Engineer – SDET, AI, Data & Platform Quality

🕒 Julho 23

🏄 California – Remoto

🏄 California – Remote

⏰ Tempo Integral

🟡 Pleno

🟠 Sênior

🔧 Engenheiro de QA (Qualidade de Software)

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Extractable

SiteLinkedInTodas as Vagas

11 - 50 funcionários

Fundada em 1999

💼 Consultoria

📣 Marketing

🏦 Bancário

Consulting • Marketing • Banking

A Extractable é uma agência de estratégia digital e design de experiência B2B que ajuda instituições financeiras a transformarem suas experiências digitais para clientes. Eles oferecem estratégia digital, experiência do usuário e design visual, estratégia de conteúdo e desenvolvimento, planejamento de tecnologia e integração de API, e análise de dados para impulsionar o engajamento, adoção e conversões para bancos, cooperativas de crédito e outras organizações de serviços financeiros. A Extractable foca na transformação digital mensurável e orientada por dados e na otimização contínua para clientes corporativos do setor financeiro.

Descrição

• Programmatic QA of Core Features • Extend our scenario test runner — a proprietary harness that captures real production personalization requests and replays them across environments, asserting on expected algorithms and content selection. • Grow it into automated regression across every client. • Write automated tests in Python with pytest across our tiers — unit, integration, HTTP, and end-to-end. • Build headless Playwright end-to-end tests to verify how personalized content and tracking render on real client pages. • Harden the pre-deploy quality gate and pre-commit checks that block bad changes automatically. • Testing & Standardizing LLMs and Agents • Design evals for non-deterministic AI features — our conversational analytics assistant, AI content builders, and generative SEO — measuring correctness, grounding, and regression across prompt and model versions. • Test the tool-calling and agentic layers — that function-calling loops pick the right tools and guardrails hold on adversarial input. • Validate our agent/MCP interface — contract conformance, rate limiting, authorization, and safe failure. • Help set our standards for shipping AI — catching hallucinations and drift, and benchmarking prompt/model changes before clients see them. • Data Quality Engineering • Build automated data-health checks that flag stale rollups, incomplete coverage, and broken aggregations before they hit a client dashboard. • Validate data pipelines end-to-end — rollups, funnel/rate/financial ingestion, and BigQuery — with drift detection across environments. • Guard model inputs so the signals our ML depends on stay accurate and complete. • Reliability & Performance • Track platform performance — response times, JS load, and page speed — and help keep it fast. • Stand up quality dashboards — uptime, coverage, data-health, and eval scores. • Collaboration & Bug Lifecycle • Work in the codebase alongside engineers to diagnose issues across development, release, and deployment. • Drive the bug lifecycle — reproduce, capture with a failing test, and verify the fix.

🎯 Requisitos

• 3+ years in QA/SDET or test automation with a code-first approach. • Strong Python — you write clean test code and can read the app you're testing. • pytest (preferred) and browser automation (Playwright or Selenium). • API and contract testing experience. • A genuine interest in testing AI — comfortable with non-determinism, evals, and prompts. • Data-savvy — strong SQL, and the instinct to validate pipelines and reconcile data. • Building automated quality gates into the deploy and release process.

🏖️ Benefícios

• Frontier work — help define what QA means for AI, agents, and data-driven personalization in finance. • Direct impact — help shape how quality works across the platform, reporting straight to the CTO. • Automation-first culture — your work is code, in the same repo and release flow as engineering. • Remote-first, collaborative, low-ego team growing with a scaling fintech.