Analytics Engineer
Linear is hiring an Analytics Engineer for a remote role open to applicants in United States. Level rates it ; you can apply on Linear.
Level's read on this job
- Uses AI ●●○○ is how Level rates this role: an ordinary role that requires AI tools.
- The posting lists this role as remote.
- Tools and skills the posting cites include OpenAI, Clay and dbt.
- Level classifies it as a senior role.
- Level lists 33 other open roles at Linear.
AI in this role
Build and scale analytics data models, pipelines, and leverage emerging tools and LLMs to accelerate development.
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
8+ 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, focused team that cares deeply about the quality of our work and the people we do it with. Here's what you can expect:
Competitive salary and equity
Employee-friendly equity terms including early exercise in the US and extended exercise windows
Daily meal and coffee stipend on every workday
Paid co-working space or desk
Health coverage (based on country requirements)
5 weeks paid vacation, plus local statutory holidays
4 months paid parental leave
Paid month off after 4 years & every 2 years thereafter
Regular team events and off-sites
Remote-first with no required commute
Learn how we think and work
A story about our mission: Read Me
Our hiring process: How we hire at Linear
How we work: Designing remote work at Linear
How Linear uses Linear: How our Customer Experience team works in Linear
A video series: Conversations on Quality
Building our teams: Why and how we do work trials at Linear
Our recent tender offer: Sharing Linear’s growth with the people building it
Linear is an equal opportunity employer. We do not discriminate based on race, color, religion, gender identity or expression, sexual orientation, national origin, age, disability, veteran status, or any other protected characteristic under applicable law.
How we rate this
Analytics Engineer at Linear rates 45 out of 100 for how much of the daily work is AI. That makes it Uses AI (AI Level 2 of 4). The level is about AI in the job, not seniority.
Uses AI. An ordinary role that requires AI tools.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Questions you could be asked
- Tell me about a project where analytics engineering was part of your work. What did you do?
- Tell me about a project where data modeling was part of your work. What did you do?
- Tell me about a project where sql was part of your work. What did you do?
- Tell me about a project where python was part of your work. What did you do?
- Tell me about a project where data pipelines was part of your work. What did you do?
Adapt your resume
- List these exact terms on your resume: Analytics Engineering, Data Modeling, SQL, Python, and Data Pipelines. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Put the AI tool in a bullet point about what you did, not just in a skills list. This role treats it as a required part of the job.
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