Software Engineer, Lakehouse (Data Platform Group)

Cato Networks is the converged network and security cloud for the AI era. 

As AI reshapes cybersecurity, enterprises face a dual challenge: unlocking AI’s potential while defending against AI-driven attacks. Cato addresses both through a unified, cloud-native platform, providing the visibility and context to prioritize risks and stop threats in real time across users, applications, data, and AI agents. 

Join us as we scale: take on ambitious technological challenges and shape the next generation of enterprise security at a company trusted by thousands of organizations worldwide. 

• End-to-end ownership of our large-scale analytical storage layer: data modeling, schema and table design, partitioning, retention, and query performance.
• Design and develop the batch processing layer over our data lake using Spark on EMR (Java and PySpark).
• Build and evolve Java/Spring Boot services that expose our data through well-defined APIs to customers and to consumers across the company.
• Own performance and cost: query optimization, file layout and compaction, cluster sizing, and storage efficiency at scale.
• Research new technologies in the lakehouse and analytical-storage space and adapt them for use in our product.
• Work closely with product, DevOps, and security teams.

• 5+ years of hands-on experience designing and developing large-scale distributed data systems in production, with a strong emphasis on performance.
• Deep, hands-on expertise in at least one of the following, at a significant scale:
  • A columnar/analytical database — ClickHouse is a major advantage, including data modeling, query optimization, and operating it in production
  • Apache Spark at an expert level, including tuning and optimizing large batch jobs.
  • Experience with open table formats such as Iceberg, Delta Lake, or Hudi
  • Experience with data lake technologies: Parquet, S3, and SQL query engines such as Athena, Trino, or Presto.
• Strong command of analytical data modeling and the design principles behind it: partitioning strategies, denormalization, batch vs. streaming trade-offs, and schema evolution.
• Strong Java and solid understanding of object-oriented design and software engineering principles.
• Experience building and running microservices on Kubernetes.
• Hands-on experience with the AWS platform, particularly EMR, S3, and Glue.
• Motivated, fast, independent learner and strong problem solver.
• A team player with excellent collaboration and communication skills.
• B.Sc. in Computer Science, Software Engineering, or a related field, or equivalent practical experience.