QA Engineer – SDET, AI, Data & Platform Quality

🕒 July 23

🏄 California – Remote

🏄 California – Remote

⏰ Full Time

🟡 Mid-level

🟠 Senior

🔧 QA Engineer (Quality Assurance)

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Extractable

WebsiteLinkedInAll Job Openings

11 - 50 employees

Founded 1999

💼 Consulting

📣 Marketing

🏦 Banking

Consulting • Marketing • Banking

Extractable is a B2B digital strategy and experience design agency that helps financial institutions transform their digital customer experiences. They provide digital strategy, user experience and visual design, content strategy and development, technology and API integration planning, and data analysis to drive engagement, adoption, and conversions for banks, credit unions, and other financial services organizations. Extractable focuses on measurable, data-driven digital transformation and ongoing optimization for enterprise financial clients.

📋 Description

• 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.

🎯 Requirements

• 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.

🏖️ Benefits

• 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.