Senior Data Platform Engineer

About the Company

Kaseya is the leading provider of AI-powered IT management and cybersecurity software, serving Managed Service Providers (MSPs) and internal IT organizations worldwide. Backed by Insight Partners, Kaseya supports customers in more than 20 countries and manages over 15 million endpoints worldwide.

Responsibilities

• Design and build real-time and batch data processing systems
• Develop event-driven data pipelines using streaming technologies
• Build scalable ingestion, transformation, enrichment, and delivery services
• Design data platform components that support millions of events and large-scale analytical workloads
• Improve reliability, observability, monitoring, and operational excellence across data systems
• Participate in architecture reviews and technical design discussions
• Own production systems, incident response, and operational improvements
• Work closely with Product, Infrastructure, Analytics, and Engineering teams
• Mentor engineers and contribute to engineering standards and best practices

Requirements

• 6+ years of experience in Software Engineering, Data Engineering, Platform Engineering, or Backend Engineering
• Strong programming experience in Java, Go, Python, Rust, Scala, or similar languages
• Experience designing and operating distributed systems in production
• Experience building and supporting streaming or real-time data platforms
• Experience with Kafka, Flink, Spark Streaming, Kinesis, Event Hubs, or similar technologies
• Experience operating cloud-native systems on AWS, Azure, or GCP
• Experience troubleshooting production issues and supporting critical systems

Preferred Qualifications

• Experience designing event-driven architectures
• Experience with CDC, event sourcing, or asynchronous processing patterns
• Experience building low-latency data systems
• Experience with infrastructure-as-code (Terraform, CloudFormation, Pulumi)
• Experience implementing CI/CD pipelines and deployment automation
• Experience with observability, monitoring, alerting, and reliability engineering
• Experience with data lakehouse platforms, Snowflake, Databricks, or large-scale analytical systems
• Experience supporting machine learning, AI, or LLM workloads