Analytics Engineer, Tailscale

• Experience designing and maintaining production-grade data models and pipelines
• Deep understanding of modern data modeling principles and best practices
• Strong SQL skills and confidence working with complex datasets and schemas
• High attention to detail, with high standards for data accuracy and validation
• Flexibility and resourcefulness in navigating manual tasks or imperfect systems
• Passion for clean code, scalable architecture, and incrementally reducing complexity over time
• Ability to manage time, prioritize effectively, and thrive in a fast-moving environment
• Clear communication skills with both technical and non-technical collaborators
• Willingness to flexibly support other domains (e.g., Product, Marketing, and Engineering) as the team grows
• Frequently cited statistics show that people who identify with historically marginalized groups are likely to apply to jobs only if they meet 100% of the qualifications. We encourage you to help us break that statistic!

Desirable

• We are looking for an Analytics Engineer who thrives in high-complexity environments—someone excited to bring clarity to chaos, untangle complex models, and lay down the foundation for scalable solutions
• This is a hands-on, high-impact role for someone who isn’t afraid to address manual processes, viewing them as opportunities to build scalable solutions that drive meaningful impact
• You’ll work closely with stakeholders across the company to build reliable, trusted data models that support reporting, analysis, and decision-making
• Dive into complex systems and manual workflows to develop an understanding of our tools and constraints
• Thoughtfully design and maintain clean, scalable, production-grade dbt models (e.g., ARR, revenue recognition)
• Work across the full data pipeline, from ingestion to transformation to visualization
• Take a pragmatic approach to system challenges, balancing quick fixes with thoughtful long-term design
• Collaborate with Finance, Engineering, Sales, and other teams across the company to understand data needs and deliver timely, practical solutions