Trumid operates a broker-dealer platform for institutional fixed-income trading (investment-grade, high-yield, and emerging-market bonds). The stack—TypeScript, React, Scala, Kafka, Apache Flink, BigQuery, dbt—reflects a fintech infrastructure built for low-latency data and real-time pricing. Active hiring across engineering and data (with senior engineering focus) paired with concurrent projects in streaming pipelines, pricing algorithms, and CI/CD tooling suggests the platform is scaling both trading volume and internal data infrastructure to keep pace with market velocity.
Trumid is a fintech broker-dealer registered with the SEC and member of FINRA and SIPC, serving institutional traders in US dollar credit markets. The platform consolidates credit trading protocols and execution tools into a single interface, targeting investment professionals trading bonds across investment-grade, high-yield, distressed, and emerging-market segments. The company operates out of New York with roughly 51–200 employees and is organized around engineering, data, and compliance functions. Core technical work spans real-time pricing engines, market-data onboarding infrastructure, and self-service analytics platforms for clients.
TypeScript, React, Scala, Python, Go, Kafka, Apache Flink, BigQuery, dbt, Fivetran, Docker, Helm, Bazel. Frontend uses Storybook, Vitest, and ag-Grid; data pipelines rely on dbt and Fivetran for transformation and ingestion.
Real-time pricing algorithms, market-data onboarding, CI/CD acceleration (Bazel/Jenkins), distributed streaming for ML, BigQuery data models, and trade surveillance. Technical pain points center on low-latency pipelines and reducing build complexity.
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