Senior Backend Engineer - MoneyLion

Gen Digital Inc.•New York, NY

3d•Hybrid

About The Position

MoneyLion is a leader in financial technology powering the next generation of personalized products and content. We pride ourselves on serving the many, not the few, providing confidence through guidance, choice, and personalization. We’re looking for a deeply experienced backend engineer who brings strong ownership, sound technical judgment, and a high bar for engineering quality. This role is about making smart architectural decisions, designing resilient services, and building the systems that power reliable customer and marketplace experiences. You will help shape and scale the backend capabilities behind MoneyLion’s product surfaces, working closely with product, analytics, and engineering teams to support partner integrations, experimentation, event quality, and core business workflows.

Requirements

Strong expertise in Java, Spring Boot, and modern backend architecture.
Comfortable designing and operating RESTful APIs, microservices, and distributed systems in production.
Strong working knowledge of SQL and NoSQL databases.
Familiarity with asynchronous and event-driven systems such as Kafka, SQS, or similar technologies.
Comfortable working in modern development environments, including Git-based workflows, containerized services, and cloud infrastructure (AWS, Kubernetes).
Strong grasp of reliability, observability, maintainability, and performance.
Familiarity with testing, CI/CD, monitoring, and modern development workflows.
Skilled at partnering with Product, Analytics, and engineering teams to deliver in a fast-paced environment.
Strong communication skills and a collaborative, problem-solving mindset.
Bachelor’s degree in Engineering, a related field, or equivalent professional experience.
5+ years in software engineering, with a track record of building and shipping production applications.
Deep expertise in Java and Spring Boot, including building and operating production-grade services.
Strong understanding of REST API design, service contracts, and backward compatibility.
Experience designing and evolving distributed systems, including service decomposition and inter-service communication.
Solid grasp of data modeling and persistence, including SQL and NoSQL tradeoffs, indexing, and query performance.
Experience with asynchronous and event-driven architectures (e.g., Kafka, SQS), including event design and reliability considerations.
Strong understanding of observability, including logging, metrics, tracing, and debugging production systems.
Experience implementing testing strategies across unit, integration, and contract testing layers.
Familiarity with CI/CD pipelines, containerization (Docker), and cloud-native environments (AWS, Kubernetes).
Excellent collaboration and communication skills, with a bias toward clear, maintainable system design.

Nice To Haves

Experience with high-scale, consumer-facing systems or marketplace platforms.
Familiarity with API gateways, rate limiting, and service-level protections.
Experience with data pipelines, event quality, and analytics instrumentation.
Hands-on experience with tools like Claude Code (or similar AI-powered IDEs) and modern AI coding models.
Practical understanding of where AI is strong vs. where it falls short, paired with sound engineering judgment.
Ability to use AI agents to explore multiple implementation approaches, evaluate tradeoffs, and converge on robust solutions without introducing fragile or “AI-shaped” systems.

Responsibilities

Help maintain a high bar for quality through design reviews, code reviews, testing, observability, and production readiness.
Partner with product, analytics, and fellow engineers to shape solutions and deliver reliably.
Design and build backend systems that are scalable, modular, and resilient to evolving product and business needs.
Strengthen reliability, maintainability, observability, and operational excellence across critical services and workflows.
Create dependable APIs, events, and service boundaries that support partner integrations, experimentation, analytics, and downstream systems.
Use AI tools for prototyping, scaffolding, refactoring, and exploratory development, then validate the output with strong engineering judgment.
Build and maintain automated test coverage across unit, integration, contract, and end-to-end layers.