Analytics Engineer

At Linear, we're building the product development system for teams and agents. AI is fundamentally changing how software gets built, and we’re shaping the tools this new era requires.

Founded in 2019, Linear has become the platform of choice for more than 40,000 companies (including OpenAI, Coinbase, and Ramp) to plan, build, and ship their products. Today, our team is distributed across North America, Europe, and Australia, and we’re continuing to grow internationally. What unites us is relentless focus, fast execution, and a deep care for software craftsmanship.

The Data team works across Linear, supporting Product, Engineering, and GTM. We own our data pipelines, warehouse, dashboards, analysis, and integrations with third-party tools. As a small team, we focus on building systems that make data accessible and useful across Linear. We’re looking for someone who wants to help shape how we architect, build, and use data as we grow.

Location & work mode

Linear is a remote-first company, with optional co-working offices in San Francisco, New York, and London. This role is open to candidates based in North America. You can work from anywhere within this region. We value deep focus and async collaboration, with intentional moments to connect in person through team off-sites, optional co-working, and occasional travel.

What you’ll do

- Work across Product and GTM (Marketing, Sales, Customer Success, and Finance) to turn ambiguous questions and operational needs into useful metrics, models, analyses, and workflows

- Build and maintain dbt models and pipelines that create trusted views of our product, customers, and business

- Design clear, maintainable data models and improve the testing, documentation, performance, and reliability of our data stack

- Build dashboards and self-service reporting in Metabase and Hex, and dig deeper when the answer requires more than a chart

- Operationalize data through reverse ETL and partner with GTM Engineering on the scoring, segmentation, automations, and internal tools that help our teams scale

- Balance fast, pragmatic answers with durable solutions, recognizing when a one-off request should become a reusable model or workflow

- Find ways to leverage emerging tools, LLMs, and coding agents to accelerate development, analysis, testing, and documentation while maintaining a high bar for correctness

What we're looking for

- 5+ years of experience in analytics engineering, data analytics, or data engineering, ideally at a fast-moving software company

- Exceptional SQL and strong hands-on experience with dbt and a modern cloud data warehouse

- Track record of owning data projects end-to-end, from shaping an ambiguous problem to shipping something people rely on

- Strong data modeling judgment: you think clearly about grain, reusable components, interfaces, dependencies, and maintainability without relying on a single prescribed methodology

- Analytical judgment: you know when to answer quickly, when to investigate deeply, and how to turn complex findings into a clear recommendation

- Comfortable moving between technical implementation and business context, from debugging a data model to understanding product adoption or sales efficiency

- High ownership mentality: self-directed, pragmatic, and willing to challenge a request or approach when something does not make sense

- Strong communication skills and experience partnering directly with both technical and non-technical teams

- Comfortable using LLMs and coding agents as part of your day-to-day development workflow

Our tech

This stack reflects the systems you’ll work in. You’re not expected to have experience with everything listed, but you should be comfortable learning quickly and working across the full lifecycle of data.

- Data Warehouse: Snowflake, dbt Cloud

- Dashboards / Analysis: Metabase, Hex

- ETL / rETL: Hevo, Fivetran

- GTM tools: Hubspot, Pocus, Clay

What we offer

We're a small, focuse