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# Senior Backend Engineer

- **Company:** French & French, LLC
- **Location:** San Francisco, CA, United States
- **Experience:** Expert
- **Salary:** $170,000.0 - $250,000.0
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Amazon Web Services, Software as a Service, Extract Transform Load (ETL), Relational Databases, Python (Programming Language), PostgreSQL, Machine Learning, Node.Js, SQL Databases, TypeScript, Snowflake, AWS Lambda, Backend, Data Layers, Data Lineage, Data Pipelines, Serverless Computing
- **Published:** October 22, 2026
- **Apply:** https://www.thejobnetwork.com/job/1ffa4727-598c-4c73-94c8-a4839e2ef15a/senior-backend-engineer

## About the Role

* 5 to 12 years of backend engineering experience, including real production ownership, not just feature work inside someone else's system
 * Strong hands-on experience with TypeScript in production, plus solid SQL and relational database skills
 * Direct experience in payments, fintech, or credit and risk systems, ideally at a fast-growing, VC-backed company
 * Comfort with AWS, serverless architecture (Lambdas), and designing for reliability at scale
 * A track record of owning a project or product area end to end: defining the problem, proposing the approach, shipping it, and iterating
 * Experience collaborating across risk, data science, and engineering, not just within a single team's boundaries
 * Bonus: Python proficiency and experience with data pipelines, ETL, or Snowflake

## Description

* Building and owning production backend systems for core payments and credit workflows, using TypeScript and Node.js end to end
 * Designing APIs, services, data models, and contracts that other teams (data science, risk, product) depend on daily, where "good enough" stops being good enough once real transactions are on the line
 * Creating internal tools that let non-engineering teams review decisions, trace data lineage, and move faster without pinging an engineer every time something looks off
 * Taking ambiguous, loosely defined problems and turning them into shipped, production-grade slices rather than waiting for a fully scoped ticket
 * Improving reliability and latency on systems where downtime or a bad edge case has a direct, visible cost, not just a Slack complaint
 * Working cross-functionally with risk and data science to define what data actually needs to look like before it hits a decisioning model
 * Owning the full lifecycle from architecture decisions through to production support, not handing off after the first deploy

Where it gets technically interesting

 * A TypeScript and Node.js backend where correctness isn't optional. This stack directly powers payment and credit decisions, so tradeoffs get scrutinized differently than in a typical SaaS product
 * Serverless architecture on AWS (Lambdas), which means thinking hard about cold starts, statelessness, and failure isolation rather than defaulting to a monolith
 * A PostgreSQL data layer feeding into Snowflake for downstream analytics and modeling, so schema decisions have consequences well past your own service
 * Designing systems and data contracts from close to zero: the infrastructure is still being defined, so you'll shape patterns other engineers will follow, not just extend someone else's work
 * Close, regular collaboration with a data science team turning your APIs and data pipelines into production, ML-driven decisions, which raises the bar on what "clean data" actually means
 * Real production stakes from day one: the systems you touch are already live with paying enterprise customers, not waiting on a future launch