Staff QA Automation Engineer

About Meeru AI

Meeru AI is building an AI-native platform that transforms how finance and accounting teams operate. We connect to enterprise financial systems — ERPs, CRMs, billing platforms, HRIS — and apply machine learning to turn fragmented operational data into grounded, auditable intelligence for CFOs, controllers, and FP&A leaders.

We deploy on customer terms — SaaS multi-tenant, SaaS single-tenant, and on-premises — across AWS, Azure, and GCP. Our customers are Fortune 500 finance teams who require data isolation, auditability, and compliance.

The Role

We are looking for a Staff Data Engineer to build and own the major subsystems of the data foundation — the pipelines, connectors, and curated models that feed the platform's analytics and AI layers. You take the data architecture and standards set by the data lead and turn them into robust, production-grade systems that don't break and don't lie about the numbers.

You own significant pieces of the platform end to end: complex transformations over the warehouse, source integrations for new customer systems, and the reconciliation and lineage that prove the curated model ties back to source. This is a hands-on, high-ownership engineering role with a clear path toward Senior Staff.

Why this role is exciting

• High ownership — own major pipelines and integrations end to end, including their performance, reliability, and cost.
• Correctness that matters — your reconciliation and lineage work is what makes the platform auditable next to real financials.
• Build for reuse — integrations are configuration against a shared framework, not throwaway scripts.
• Multi-cloud, multi-tenant — pipelines run inside customer clouds with strong isolation and no data egress.

Requirements

• 8+ years in QA and test automation, including 3+ years leading people. You've hired, run 1:1s and performance reviews, and mentored QA engineers, not just led a project.
• You've built automation frameworks from scratch. You've taken a product from little or no automation to a working framework across API, UI and backend tests, and you can explain the design choices.
• Strong coding in Python and JavaScript/TypeScript. You write maintainable test code daily in both: PyTest for the backend, TypeScript for the frontend.
• Deep API testing experience. REST, GraphQL and WebSockets, using tools like PyTest, Postman or REST Assured, including contract, negative and auth testing.
• Modern end-to-end UI testing. Playwright or Cypress against React/Next.js apps, with reliable, low-flake suites covering critical user journeys.
• Backend and database testing. Testing Python/FastAPI services and PostgreSQL (SQLAlchemy), with SQL-based data validation and attention to database performance.
• CI/CD integration. You've wired tests into GitHub Actions, Jenkins or GitLab CI, set up deployment quality gates, and worked with Docker/Kubernetes environments.
• Performance and load testing. You've designed load, stress and scalability tests with tools like k6, Locust or JMeter, plus front-end checks like Lighthouse.
• Security testing. SAST/DAST in the pipeline (e.g. SonarQube, Snyk, OWASP ZAP, Burp Suite), and testing authentication, role-based access and data encryption.
• Multi-tenant SaaS experience. You've tested platforms where many customers share infrastructure and know how to prove one tenant can't see another's data.
• Compliance-driven testing. You've supported SOC 2, GDPR or HIPAA requirements with test processes and evidence.
• Cloud experience. AWS preferred (EC2, ECS, EKS, Lambda); GCP or Azure also welcome.
• Quality leadership and process. You've defined QA standards, metrics and KPIs, release and regression procedures, and runbooks, and you report quality clearly to leadership.
• Clear communication and collaboration. You work closely with Product on acceptance criteria, Engineering on testability and DevOps on releases, explaining risk to technical and non-technical people alike, in an Agile/Scrum setup.

Nice to have

• Testing AI/LLM applications. You've tested LLM features or agentic workflows, can handle non-deterministic output, validate prompts and model outputs, and check for bias and fairness. LangChain/LangGraph familiarity is a plus.
• FinTech or financial services background. Experience where accuracy, audit trails and reliability around money matter.
• Startup experience. You're comfortable building from zero in a fast-moving, ambiguous environment.
• Observability and production monitoring. Datadog, CloudWatch, Prometheus or New Relic, including monitoring AI behavior in production.
• Chaos engineering and resilience testing.
• Test management tools. TestRail, Zephyr or Jira-based test management.
• Certifications. ISTQB Advanced (or similar), AWS Certified Developer or Solutions Architect.
• Community involvement. Open-source contributions to testing frameworks, technical writing or conference talks.