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OpenFX is building infrastructure to move money as freely as data across borders, unrestricted by time zones or legacy systems. The company processes $80B in annualized transaction volume and operates regulated entities across the US, UK, EU, UAE, and beyond. The team includes veterans from J.P. Morgan, Goldman Sachs, Visa, Mastercard, Coinbase, Stripe, and PayPal, backed by tier-one investors including Accel, Atomico, and Lightspeed.
You will own backend quality engineering for the Banking team, covering systems at the core of OpenFX's money movement: ledgers, payouts, reconciliation, settlement, and related services. This is a hands-on SDET role with significant ownership over testing complex backend systems. You will work directly with backend engineers and product teams to build automation into the development lifecycle, identify failure modes early, and ensure critical money flows remain correct under real-world conditions.
You will inherit a growing automation setup with significant room to shape architecture, tooling, quality standards, and testing strategy. As transaction volume and system complexity increase, the goal is to move from testing individual flows to building a scalable quality engineering system that gives teams confidence to ship quickly without compromising financial correctness. You will report to the Engineering Manager for Banking.
Key responsibilities include: designing and evolving scalable automation frameworks for APIs, services, databases, and end-to-end banking workflows; building test scenarios covering transaction processing, ledger consistency, reconciliation, settlement, and financial calculations; validating failure behavior including retries, idempotency, concurrency, timeouts, and recovery paths; integrating automated tests into CI/CD pipelines and improving execution speed; building and maintaining test environments, data setup, mocks, and containerized test runners; driving performance testing using tools like JMeter or k6; improving debugging and observability through logs, metrics, and traces; partnering early in development to identify risk areas and testability gaps; applying AI tools for test generation and synthetic data creation; and mentoring junior SDETs on framework design and testing standards.
REQUIREMENTS:
Must-haves:
- Built or significantly extended backend automation frameworks (not just individual test cases)
- Strong expertise testing REST APIs, backend services, databases, and service-to-service integrations
- Ability to reason about distributed-system failure modes: retries, idempotency, concurrency, timeouts, duplicate processing, partial failures
- Can validate complex workflows across APIs, relational databases, events, queues, and downstream services
- Comfortable writing complex SQL queries to validate data integrity and system behavior
- Understand how to integrate automated testing into CI/CD pipelines and design fast, stable, actionable test suites
- Strong fundamentals in debugging, clean code, Git, and data structures/algorithms
- Can independently understand complex systems, identify high-risk areas, and translate them into effective test strategies
- Can work closely with backend engineers and challenge designs constructively on reliability, correctness, and testability
What helps you stand out:
- Experience testing payments, banking, fintech, or other correctness-critical systems
- Production-quality automation code in Java, TypeScript, JavaScript, or Python with strong software engineering fundamentals
- Experience validating ledgers, reconciliation, settlement, payouts, or transaction accounting
- Understanding of double-entry accounting or financial ledger principles
- Experience with Java/RestAssured, TypeScript/JavaScript automation frameworks, Maven, or TestNG
- Experience testing API authentication and security (OAuth, HMAC signing)
- Experience with event-driven architectures, message queues, and asynchronous workflows
- Hands-on AWS experience (S3, CloudWatch, EKS)
- Experience running test infrastructure on Docker or Kubernetes
- Experience with performance/load-testing tools (JMeter, k6)
- Experience building mocks, simulators, service virtualization, or sophisticated test-data tooling
- Strong observability experience across logs, metrics, traces, and production monitoring
- Thoughtful use of AI tools (Copilot, LLMs) to improve engineering velocity and automation quality
