Backend Software Engineer

• As a Platform Software Engineer at Logic Gate, you will build the robust backend services, APIs, and data layers that power Risk Cloud. Working autonomously within an assigned domain, you will design stable solutions, ship high-impact features, and systematically debug production workloads
• This role is ideal for an engineer who executes with efficiency and takes operational ownership within their scope of responsibility, while actively developing the depth to shape our future enterprise cloud architecture
• Feature Engineering:
  Design and build backend features and robust REST APIs on the Risk Cloud platform using Java, Kotlin, and Spring Boot—taking engineering tasks from requirements through implementation, testing, and production release
• Quality Assurance & Testing:
  Write clean, understandable code and build comprehensive unit and integration tests using JUnit, adhering to the testing pyramid to contribute to a highly stable, dependable deployment lifecycle
• Systematic Debugging:
  Independently diagnose and resolve issues within your owned service area by reading distributed logs, analyzing console data, tracing request flows, and utilizing customer session analytics without needing escalation for routine anomalies
• Domain Ownership:
  Act as a subject matter expert (SME) for a defined portion of the platform (such as our core workflow engine or reporting services), gathering context independently to design functions optimized for maintainability and scalability
• Architecture & Documentation:
  Participate productively in Technical Specification reviews, estimate level of effort accurately, and author clear technical documentation including API contracts, data models, and Confluence runbooks
• Data Management:
  Work operationally with relational databases (PostgreSQL) and advanced platform data stores (Neo4j, Redis) alongside messaging infrastructure (RabbitMQ) to trace, query, and optimize how data flows across microservices
• Agile
  
  Collaboration:
  
  Partner cross-functionally with product managers, QA engineers, and frontend developers in Agile sprints, mapping out individual task prioritization while raising dependencies or project blockers daily to ensure engineering alignment
• Code Reviews & Culture:
  Participate actively in regular peer code reviews—providing meaningful praise, ensuring compliance with team conventions, and receiving constructive feedback non-defensively with an innate curiosity to learn

• 47 days off starting your first year, including paid volunteer time
• 12 weeks parental leave
• $1,000 annual educational stipend
• Ongoing learning and development
• Stock options and annual performance bonus opportunities

Core Stack Proficiency:
Strong development experience using Java, Kotlin, or a comparable enterprise JVM language, alongside practical exposure to frameworks like Spring Boot Database Fundamentals:
Practical experience working with relational databases (such as PostgreSQL), including writing complex queries, executing schema migrations, and analyzing query performance AI-Augmented Development:
Practical experience using AI coding assistants (Cursor, Git Hub Copilot, Codex, or Claude Code) as part of a normal workflow—writing effective prompts, building reusable skills or custom instructions, and knowing when to trust, verify, or discard AI-generated output

Professional

Experience:

3-5 years of professional backend, platform, or systems engineering experience within a high-growth environment

Autonomous Troubleshooting:
Demonstrated ability to independently debug complex distributed backend services by parsing logs, utilizing monitoring systems like Datadog, and verifying compliance protocols via security tools like Git Lab Ultimate, Sonarqube and Wiz.

API Design Mastery:
Solid experience building, owning, and maintaining production-grade REST APIs, with an emphasis on API versioning, contract stability, and cross-team consumer workflows

Polyglot Exposure:
Experience or fluency with Go (our platform chapter utilizes Go alongside our primary JVM stack)
Advanced Data

Infrastructure: Familiarity with graph databases (Neo4j), message queues (RabbitMQ), or distributed caching layers (Redis)
SaaS Architecture:
Background building multi-tenant B2B SaaS applications where strict data isolation, high availability, and performance at scale are paramount

Modern Distributed Systems:
Deep understanding of event-driven architectures, microservices patterns, or containerized applications at production scale