Teams at 73 Strings

Build and own test infrastructure for distributed financial-data services, including frameworks, CI/CD execution, ephemeral Kubernetes environments, contract and end-to-end testing, performance testing, test-data management, and quality signals. Develop testing approaches for AI-enabled features using probabilistic assertions and evaluation harnesses. Improve reliability and reduce flakiness, establish engineering-wide testing practices, enable teams through tooling and documentation, and partner with SRE and AI evaluation engineers.

Own backend integration services connecting market data providers, client systems, and internal platforms. Build reliable APIs and ingestion services, manage distributed systems on Kubernetes, and ensure performance, observability, security, and correctness. Responsibilities span design through production operations, including CI/CD, incident response, SLOs, and cost management. The role requires strong Java and Python skills, financial data experience, API design expertise, and practical use of AI engineering tools.

Own and deliver the foundational data platform and core data products: translate strategy into shipped pipelines and products, establish product practices (intake, contracts, SLAs), manage roadmap and prioritization, produce high-quality requirements, and drive adoption and business impact across distributed engineering teams.