Senior Quality Assurance Automation Engineer

Job Description

The Role

We're hiring an SDET to help us ship a large-scale Analytics Data Platform composed of multiple services — data ingestion and processing pipelines, analytics APIs, web applications, and GenAI-powered features on top. You'll be the engineer who makes sure all of this stays reliable, fast, and trustworthy as it grows.

Testing GenAI components — where deterministic assertions are no longer enough — is part of the job, alongside the analytics, data, and application layers that make up the platform.

What You'll Do

• Design, build, and own automation frameworks across API, UI, integration, and end-to-end layers.
• Write production-grade test code that engineers respect — reviewed, versioned, refactored, and treated as a first-class part of the codebase.
• Integrate test suites into CI/CD pipelines and continuously cut down feedback loop time for the development team.
• Contribute to test environments and test data management.
• Improve our shift-left practices: integration and E2E testing, ephemeral environments, preview deployments — the foundation is already in place; we want it sharper, faster, and more reliable.
• Use AI agents and assistants in your day-to-day work — our team actively builds with them for development and testing, and we want you to keep pushing that practice forward.

• Help set the quality bar: what done looks like, what gets automated, what stays manual exploratory, and why.
• Communicate quality signals clearly to product, engineering, and stakeholders — from flaky-test reports to release readiness.

• 5+ years in software QA, with at least 3 years writing and maintaining production-grade test automation code.
• Strong programming skills in at least one of: Python, JavaScript/TypeScript, or Java. You treat test code as production-grade code.
• Hands-on with modern automation frameworks: Playwright with advanced fixtures, modern JUnit versions with JUnit Extensions, pytest, Vite/Jest, or equivalent.
• API testing depth: REST, WebSocket, contract testing.
• CI/CD fluency: GitHub Actions, GitLab CI, Jenkins, or similar — you've built and maintained pipelines, not just consumed them.
• Working knowledge of Docker — enough to spin up test environments, build images, and reason about networking.
• Strong Git, code review, and software engineering hygiene.
• Excellent written and verbal communication — you can articulate risk and quality to non-engineers.

• Big data / analytics testing: SQL, Spark, ElasticSearch, data quality, data lineage.
• Performance testing at scale (k6, Locust, JMeter, Gatling) for data- or AI-heavy workloads.
• Observability fluency: distributed tracing, structured logs, metrics — OpenTelemetry, Prometheus, Grafana, Datadog.
• Cloud platforms: AWS, Azure, or GCP at the level of provisioning your own test infrastructure.

• Engineer's mindset — you'd rather fix a flaky test root cause than retry it three times in CI.
• Curiosity about AI and agentic systems — you've already tried building something with an LLM, even if it's a side project.
• Pragmatism over dogma — you know when to write the eval and when to just ship a manual check.
• Bias toward fast feedback loops — you treat 30-minute CI as a bug.
• Comfort with ambiguity — testing non-deterministic AI systems means living with grey areas, and you're fine designing for that.

• Kubernetes experience — debugging pods, reading manifests, working with Helm or kustomize.
• Open-source contributions — to test tooling, eval frameworks, agentic libraries, or anywhere else.
• Public writing or talks on test automation, AI evaluation, or quality engineering.
• Experience in a regulated or high-stakes domain (gov, finance, healthcare, security).
• Familiarity with security and privacy testing for AI systems (prompt injection, PII leakage, jailbreak resilience).