Analytics Engineer, Data
Job description
About the role
The work supports simplifying networking by reducing complexity for a distributed workforce. Collaboration with product and operations teams happens in a fully remote environment.
Data roles turn raw information into decisions. Analysts query databases and build dashboards. Data scientists build models that predict outcomes. Data engineers build the pipelines that move and store data. All three work closely with business teams and need a mix of statistics, coding, and communication. Nearly every modern company runs on data teams, from startups to banks. A strong portfolio of past analyses matters more than degrees in many hiring decisions.
Key facts
What you'll do
Data pipelines are built and maintained to deliver reliable inputs for product and business analysis.
Datasets are transformed to measure feature adoption and guide roadmap priorities for product teams.
Tailscale-specific tooling is used to query and visualize data that reflects network performance and user patterns.
Experiment results are evaluated to inform iteration and validate the impact of product changes.
Operational metrics are defined and tracked to support capacity planning and service reliability.
Requirements
A Bachelor's degree in a quantitative field or equivalent practical experience is required.
You have experience in analytics or data roles within software or infrastructure products.
Strong proficiency in SQL is required to transform raw events and build analytical datasets.
Fluency in a programming language such as Python or R is required for data manipulation and automation.
Experience with visualization tools like Looker or Tableau is required to communicate findings to non-technical audiences.
Comfort working with networking concepts and logs is required to analyze Tailscale-specific telemetry.
Practical notes
This role is remote based in Canada and aligned with Tailscale team hours.
Typical interview steps
Data interviews commonly include a SQL or coding exercise, a statistics question, and a case study. Candidates may be asked to design a metric, interpret an experiment, or build a small model. Some companies give a take-home analysis. Expect questions about past projects and the business impact of your work. Interviewers often evaluate how you communicate uncertainty and business impact, not only the math. Bringing a clean write-up of a past analysis to the interview is well received.
Good to know
Analytics roles in infrastructure focus on reliability, security, and network operations data.
Proficiency in distributed systems concepts helps interpret metrics from global node interactions.
Familiarity with networking stacks and protocol behavior supports accurate analysis of user journeys.
Data visualization skills translate technical findings into clear narratives for diverse stakeholders.
Remote collaboration tools are central to sharing insights and coordinating with cross-functional teams.
Questions to ask
Useful questions for the interview: what a typical week looks like, how work is assigned, what tools the team uses, and how feedback works. Asking how the role has changed recently and what the team wishes it had known when joining is also reasonable. Questions about the manager's priorities are especially valued.
Career growth
Data careers grow toward senior analyst, staff data scientist, or data engineering lead. Many professionals specialize in machine learning, analytics, or infrastructure. Cross-functional work with product and engineering teams becomes more important at senior levels. The field changes quickly, so continuous learning is part of the job. Professionals who can translate numbers into decisions tend to advance fastest.
About the company
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.