Senior Fullstack Engineer, Growth

Nivoda•Dover, NH

1d•$200,000 - $280,000•Remote

About The Position

Nivoda is seeking a Senior Fullstack Engineer for their Growth Pod. This role is responsible for the entire customer journey from discovery to becoming a valuable customer, including sign-up, onboarding, verification, activation, and ongoing engagement. The engineer will own both backend systems and front-end experiences, focusing on services and screens that impact conversion. The role involves working with event-driven backend systems that track account states in real-time and creating seamless front-end experiences. This position requires digging into data to identify and solve conversion issues, working with sensitive credit and payment data, and collaborating with identity verification and risk providers. The engineer will also be involved in designing and running experiments, performing cohort analysis, and diagnosing funnel problems. The role emphasizes using AI agents to accelerate building, testing, and analysis, with a critical evaluation of AI-generated output, especially concerning sensitive data. Key responsibilities include owning and evolving backend services for onboarding, verification, and activation; designing and building event-driven systems to manage account states; integrating with third-party identity verification and risk providers; handling credit and payment data responsibly; designing and running experiments; performing cohort and funnel analysis using SQL and data warehouses; and tackling ambiguous problems without a predefined playbook. The role also involves using AI agents for building, testing, and analysis, with a strong emphasis on critically evaluating AI-generated output.

Requirements

4 - 7 years of experience
Strong backend product engineering experience, most likely in TypeScript/Node.js, with real ownership of funnel-critical, production services
Hands-on experience with event-driven architectures and workflow orchestration — workflow engines or event pipelines that evaluate state continuously and trigger actions automatically
Genuine experimentation rigor: you've designed experiments properly, you understand guardrail metrics, and you know when a result is real versus noise
Strong SQL and warehouse fluency (Snowflake or similar), and the confidence to do your own cohort and funnel analysis rather than waiting on a data team
Comfort using AI agents to build and test software and to draft and sanity-check analytical work, paired with the judgement to catch the subtle errors a reasonable-looking AI output can hide
High tolerance for ambiguity — you're energised by problems that don't have an established playbook, not just ones with a clear spec
Practical fluency evaluating AI-generated output for fitness before it ships, whether that's copy, messaging, or a suggested root cause

Nice To Haves

Direct experience integrating with identity verification, KYB/KYC, or fraud and risk providers
Fintech-adjacent experience handling credit, payment, or other sensitive financial data responsibly
Experience in a high-growth marketplace or fintech environment with similar account lifecycle complexity
Familiarity with workflow/orchestration tooling such as Temporal, AWS Step Functions, or event-streaming platforms like Kafka

Responsibilities

Own and evolve the backend services behind onboarding, verification, activation, and the broader sign-up-to-purchase funnel, most likely in TypeScript/Node.js given our stack
Design and build event-driven, workflow-orchestrated systems that evaluate account state continuously — activation, slipping, winback, lapsed, churned — and trigger the right action automatically, rather than relying on batch scripts
Integrate with third-party identity verification, KYB/KYC, and fraud/risk providers, and make sure those integrations are resilient, well-monitored, and fail safely
Handle credit and payment data responsibly as part of personalization and reactivation logic, with full awareness of what’s at stake if that logic is subtly wrong
Design and run experiments properly — including defining guardrail metrics and knowing when a result is real versus noise — for things like referral incentives, reactivation offers, and onboarding changes
Write your own SQL and work directly in the warehouse (Snowflake or similar) to run cohort analysis, diagnose time-to-activation and time-to-churn, and get to the bottom of funnel problems without waiting on a data team
Take on problems that don’t have an established playbook — like a credit utilization gap or a signup-to-purchase conversion decline — and help define the problem, not just solve a given brief
Use AI agents to accelerate building and testing funnel and lifecycle services, with extra care reviewing anything that touches credit, payment, or verification logic
Use AI to help draft and sanity-check your own analytical queries, while owning full responsibility for verifying results before they inform a real decision
Evaluate AI-generated output — personalized messaging, reactivation copy, churn-cause suggestions — critically, rather than shipping whatever the model produces uncritically