Senior Software Engineer

Kaseya is the leading provider of AI-powered IT management and cybersecurity software, serving Managed Service Providers (MSPs) and internal IT organizations worldwide. Our comprehensive platform helps organizations efficiently manage, secure, and automate their IT environments, driving operational efficiency and long-term business success.

Backed by Insight Partners, a leading global software investor, Kaseya has experienced sustained double-digit growth and continues to expand its global footprint. Today, Kaseya supports customers in more than 20 countries and manages over 15 million endpoints worldwide.

Founded in 2000, Kaseya has built a culture centered around innovation, accountability, and results. We are a high-growth, high-performance organization that values individuals who are driven, adaptable, and committed to delivering exceptional outcomes for our customers and teammates alike.

At Kaseya, success comes from embracing challenges, moving with urgency, and continuously raising the bar. 

Location: Toronto, ON
Work Model: Toronto-based; candidates must be local to Toronto or willing to relocate

Join a fast-growing company that’s transforming the IT industry. At Kaseya, you’ll have the opportunity to work with cutting-edge technology, collaborate with a dynamic team, and develop your career in a high impact role.

Join the Kaseya growth rocket ship and see how we are #ChangingLives!

We’re hiring a Senior Software Engineer to help build the core platform services that support Kaseya’s next generation of SaaS products. This is a backend platform role focused on distributed systems, event-driven architecture, cloud-native services, and production reliability. You’ll work closely with Platform, Data, AI, and Product Engineering teams to build services that support multiple products, customers, and engineering teams at scale.

• →Design, build, test, deploy, and operate backend platform services used across multiple SaaS products
• →Build scalable APIs, microservices, event-driven systems, and internal platform capabilities
• →Develop systems that handle retries, idempotency, ordering, backpressure, failure recovery, and replay
• →Improve multi-tenant SaaS capabilities, including tenant isolation, RBAC, auditability, and security
• →Own production systems, including monitoring, alerting, debugging, incident response, and post-incident improvements
• →Partner with Data and AI teams to support reliable data flows, workflow orchestration, RAG systems, and production AI workflows
• →Work with cloud-native infrastructure, including AWS, Azure, or GCP, Kubernetes, Docker, Terraform, and CI/CD pipelines
• →Participate in design reviews, code reviews, technical planning, and mentorship across the engineering team

• 6+ years of backend software engineering experience building production systems using Java, Go, Python, Kotlin, C#, TypeScript, or similar technologies
• Experience designing and operating distributed systems or multi-tenant SaaS platform services
• Experience building APIs, microservices, and event-driven systems using messaging or streaming technologies such as Kafka, RabbitMQ, SQS/SNS, Pub/Sub, or similar
• Experience working with cloud platforms such as AWS, Azure, or GCP and containerized deployments using Docker or Kubernetes
• Experience owning production services, including monitoring, incident response, debugging, and root-cause analysis

• Experience with multi-tenant SaaS capabilities such as RBAC, authorization, tenant isolation, auditability, or access control
• Experience with relational or NoSQL databases such as PostgreSQL, MySQL, DynamoDB, Cassandra, MongoDB, Redis, Elasticsearch, ClickHouse, or Snowflake
• Experience with observability tools such as Datadog, Prometheus, Grafana, OpenTelemetry, Splunk, CloudWatch, or similar
• Experience with workflow orchestration tools such as Temporal, Airflow, Step Functions, or similar
• Experience with CQRS, event sourcing, saga patterns, distributed transactions, retries, DLQs, or data consistency trade-offs
• Experience building data platforms, event ingestion systems, analytics platforms, or internal platforms used by multiple engineering teams
• Experience supporting production AI, RAG, LLM orchestration, evaluation, guardrails, observability, or reliability workflows