Senior Fullstack Engineer, Growth
New
N
NivodaJewellery marketplace
Location: USFull-TimeSenior
Salary200,000 - 280,000 USD per year
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Job Details
- Experience
- 4 - 7 years
- Required Skills
- Node.jsSQLSnowflakeTypeScript
Requirements
- 4–7 years of experience.
- Strong backend product engineering experience, most likely in TypeScript/Node.js.
- Experience owning funnel-critical production services.
- Hands-on experience with event-driven architectures and workflow orchestration.
- Experience designing experiments, defining guardrail metrics, and distinguishing meaningful results from noise.
- Strong SQL and data warehouse fluency, such as Snowflake or similar.
- Ability to conduct cohort and funnel analysis independently.
- Comfort using AI agents to build and test software and draft or sanity-check analytical work.
- Judgement to identify subtle errors in AI-generated output and verify analytical results.
- High tolerance for ambiguity and problems without an established playbook.
- Practical ability to evaluate AI-generated copy, messaging, and suggested root causes before use.
- Nice to have: experience integrating identity verification, KYB/KYC, or fraud and risk providers.
- Nice to have: experience handling credit, payment, or other sensitive financial data.
Responsibilities
- Own and evolve backend services for onboarding, verification, activation, and the sign-up-to-purchase funnel.
- Build event-driven, workflow-orchestrated systems that evaluate account states and trigger actions automatically.
- Integrate third-party identity verification, KYB/KYC, and fraud or risk providers, ensuring integrations are resilient, monitored, and fail safely.
- Handle credit and payment data responsibly in personalization and reactivation logic.
- Design and run experiments, including defining guardrail metrics and assessing whether results are meaningful.
- Use SQL and the data warehouse to run cohort analysis and diagnose activation, churn, and funnel issues.
- Use AI agents to accelerate building and testing services and analytical queries, and verify outputs before relying on them.
- Evaluate AI-generated messaging and churn-cause suggestions before they are used.
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