Analytics Engineer, Data

Tailscale delivers software that securely interconnects people and their devices, making safe connections effortless. The Analytics Engineer will build reliable, trusted data models and scalable data solutions to support reporting, analysis, and decision-making while collaborating with stakeholders across the company.

Responsibilities:

• 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

Requirements:

• • 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
• • Experience at a high-growth PLG SaaS company
• • A track record of transforming manual, ad hoc workflows into scalable data pipelines
• • Familiarity with billing systems, GAAP accounting, revenue recognition, or audit support
• • Exposure to Stripe, Salesforce, or Orb in billing and finance contexts
• • Familiarity with financial cycles and the need for fast iteration during month- and quarter-end