T

Analytics Engineer, Data

Tailscale
9 days ago
Full-time
Remote
Worldwide
Remote Data

About Tailscale 

Tailscale is building the new Internet by delivering software that makes it easy to securely interconnect people and their devices, no matter where they are. From hobbyists to multinational corporations, teams of every size use Tailscale each day to protect their networks, share access to internal tools, and more. We're building a future for the Internet that's easy, sensible, and safe, like it used to be. Founded in 2019 and fully distributed, we're backed by Accel, CRV, Insight, Heavybit, and Uncork Capital.

Job Description

We’re looking for a hands-on Analytics Engineer who thrives in fast-moving, high-complexity environments and enjoys bringing clarity to ambiguity. You’ll join a growing data team with many open, high-impact challenges across billing, finance, product, GTM, and business operations.

You’re someone who asks thoughtful questions, goes deep to understand how things work, and loves transforming loosely defined user problems into scalable, reliable data solutions. You’re not afraid to challenge assumptions, rethink processes, or dive into complex systems. You help teams work better through clean models, automated workflows, and simple, trustworthy data.

This role is ideal for someone with solid experience delivering production grade analytics engineering work who is excited to take on broader ownership and grow their scope over time as the team expands.

Key Responsibilities

  • Partner with teams across Finance, Engineering, Sales, Product, Marketing, and Customer Success to uncover their workflows, challenges, and data needs.
  • Design, build, and maintain scalable dbt models and data pipelines supporting financial reporting (ARR, billing, revenue recognition) as well as operational, product, and GTM analytics.
  • Tackle ambiguous, high-complexity problems, untangling systems, manual processes, and unclear logic to produce clear, durable data solutions.
  • Translate user challenges and incomplete requirements into structured data models, automated workflows, and end-to-end solutions.
  • While building towards automation, understanding the user needs to process manual tasks.
  • Own solutions from ingestion to transformation to data quality, documentation, and stakeholder enablement.
  • Connect data and logic across domains, identifying how systems relate and surfacing opportunities to simplify, standardize, or automate.
  • Advocate for high standards in data quality,