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Eisen is seeking a skilled engineer based in New York to build testing infrastructure for their financial systems. You will design and develop a comprehensive testing harness that ensures quality and reliability as the company scales.
Your experience with CI/CD pipelines, strong programming skills in JavaScript and TypeScript, and understanding of distributed systems will be key as you help shape the future of Eisen's operations.
If you have a background in fintech or a similarly regulated environment, this role offers a competitive salary and impressive benefits, including health coverage and unlimited PTO.
We process billions of financial records for major banks. Peak ingestion is about 2,500 records per second across MongoDB, Kafka, Bull, Redis, and Snowflake. We need to scale that 10x.
We need someone to build the infrastructure that makes that possible without breaking things. This role focuses on reliability, test infrastructure, and deployment confidence
This is greenfield work. Nobody owns this full-time today. You would be the first person whose entire job is building the systems that keep quality high as we ship faster.
Eisen is the first account offboarding platform for financial institutions. We handle escheatment, disbursement, and customer outreach for banks and fintechs. We help them close the loop on inactive accounts and get people back to their money.
We handle SSNs, account numbers, and financial data. If we get something wrong, a bank’s customers are affected and regulators notice. We’re SOC 2 compliant, onboarding bigger financial institution customers, and adding new products constantly.
Your first project is designing and building our testing harness from the ground up. This is infrastructure work, not configuring someone else’s framework. You’ll architect the system, choose the tools, and own the outcome.
After that, the roadmap looks something like this:
You come from infrastructure, DevOps, platform engineering, or SRE. You’ve built systems that other engineers depend on. You’re used to thinking about pipelines, reliability, observability, and automation at scale. Now you want to apply that to quality.