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Kaseya is seeking a Staff Software Engineer to lead the design and delivery of Java-based SaaS platforms and shared backend services. This is a hands-on technical leadership role focused on solving complex distributed systems challenges, owning critical platform capabilities, and improving the scalability, reliability, and maintainability of production applications.
You will serve as the technical owner for complex services and platform capabilities from initial design through production operation. Responsibilities include designing and delivering Java-based platform services, APIs, distributed systems, and event-driven applications; leading technical initiatives involving multiple services, engineering teams, or product dependencies; and remaining hands-on through implementation of critical components, technical prototyping, code reviews, and production troubleshooting.
You will partner with Product, Engineering, Security, and Architecture teams to translate business requirements into scalable technical solutions, improve system performance, availability, observability, security, and operational readiness, and contribute reusable patterns and engineering standards for Java development, API design, testing, CI/CD, and production support. Mentoring Senior and mid-level engineers through design reviews, technical guidance, and knowledge sharing is also part of the role.
Required qualifications include 8+ years of software engineering experience building production SaaS applications, distributed systems, or enterprise software platforms; 5+ years developing and operating production backend services using Java; experience serving as the technical owner for a production service or platform capability; experience designing distributed systems deployed on AWS, Azure, or GCP; and experience leading technical design reviews or engineering initiatives spanning multiple services or teams.
Preferred qualifications include experience with Java technologies such as Spring Boot, Spring Cloud, Hibernate, Kafka; designing cloud-native, microservices-based, or event-driven systems; relational databases such as PostgreSQL, MySQL, SQL Server, or Oracle; platform modernization or cloud migration initiatives; improving reliability, observability, performance, or operational practices; integrating AI-enabled capabilities into production software; and using AI-assisted development tools.
