Linear
Analytics Engineer
About this role
Linear seeks an Analytics Engineer to build and maintain the data infrastructure supporting Product, Engineering, and Go-to-Market functions. You'll design dbt models, create dashboards, and turn business questions into actionable metrics while working with a distributed team across North America.
What you'll do
- Build and maintain dbt models and data pipelines for product, customer, and business metrics
- Design maintainable data models and improve testing, documentation, and performance across the data stack
- Create dashboards and self-service reporting in Metabase and Hex for cross-functional teams
- Operationalize data through reverse ETL and partner on GTM scoring and segmentation workflows
- Balance rapid analysis with durable solutions, converting ad-hoc requests into reusable components
- Explore emerging tools and LLMs to accelerate development and analysis workflows
What they're looking for
- SQL (expert level)
- dbt and cloud data warehouse (Snowflake preferred)
- Data modeling and architecture
- Metabase and Hex dashboarding
- ETL/rETL tools (Fivetran, Hevo)
- Business analytics and metrics design
- Python or similar programming language
- LLMs and coding agents
Benefits
- Competitive salary and equity with favorable exercise terms
- Daily meal and coffee stipend
- Paid co-working space or home desk
- Health coverage
- 5 weeks paid vacation plus statutory holidays
- 4 months paid parental leave
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Linear
Linear builds a product development platform used by thousands of companies, with a focus on AI-powered features and modern development workflows. The company is hiring full-stack engineers to build these capabilities and developer marketers to showcase their platform's AI and features through authentic, multi-format content.
- Website
- linear.app
Likely interview questions
- Walk us through a dbt project where you redesigned a data model for maintainability—what made it better and how did you communicate the changes?
- Describe a time you converted an ambiguous business question into a clear metric or analysis. How did you validate your approach?