Software Engineer Ii, Data Automation

Klaviyo Klaviyo · Enterprise · Boston, MA · Engineering

Software Engineer II, Data Automation at Klaviyo. This role focuses on building tooling for Data Platform engineers to provision and test infrastructure for core Klaviyo functionality and data-driven insights. The team operates a real-time data platform on AWS, using technologies like Python, Terraform, Kubernetes, and various databases and data processing tools. While the role is primarily engineering focused on infrastructure and data tooling, it explicitly mentions experimenting with AI tools and workflows to improve efficiency, indicating an 'Exploring' AI maturity signal and an AI score of 5 (using AI tools but not building them as the core craft).

What you'd actually do

  1. designing, building, and maintaining systems that help us operate our mission critical analytics infrastructure
  2. work with a cross functional team to help design systems, create infrastructure, write high quality code, and operate large scale systems in a production environment

Skills

Required

  • Degree or equivalent experience in a software engineering discipline
  • Proficient in using at least one modern programming language
  • Able to communicate well
  • Able to work with others on a team
  • Able to learn continuously and adapt to a high growth environment
  • Able to stick with and solve difficult problems
  • You’ve already experimented with AI in work or personal projects, and you’re excited to dive in and learn fast. You’re hungry to responsibly explore new AI tools and workflows, finding ways to make your work smarter and more efficient.

Nice to have

  • Experience designing, building, and operating distributed systems in a cloud computing environment
  • Experience provisioning infrastructure using Terraform or a similar infrastructure as code tool
  • Experience performance tuning and scaling systems
  • Experience with OLTP and OLAP databases
  • Experience with high volume data processing and storage systems
  • Experience using Linux operating systems

What the JD emphasized

  • experimented with AI in work or personal projects
  • excited to dive in and learn fast
  • hungry to responsibly explore new AI tools and workflows
  • finding ways to make your work smarter and more efficient