About the role

Grafana Labs is a remote-first, open-source powerhouse. There are more than 20M users of Grafana, the open source visualization tool, around the globe, monitoring everything from beehives to climate change in the Alps. The instantly recognizable dashboards have been spotted everywhere from a NASA launch and Minecraft HQ to Wimbledon and the Tour de France. Grafana Labs also helps more than 3,000 companies -- including Bloomberg, JPMorgan Chase, and eBay -- manage their observability strategies with the Grafana LGTM Stack, which can be run fully managed with Grafana Cloud or self-managed with the Grafana Enterprise Stack, both featuring scalable metrics (Grafana Mimir), logs (Grafana Loki), and traces (Grafana Tempo).

We’re scaling fast and staying true to what makes us different: an open-source legacy, a global collaborative culture, and a passion for meaningful work. Our team thrives in an innovation-driven environment where transparency, autonomy, and trust fuel everything we do.

You may not meet every requirement, and that’s okay. If this role excites you, we’d love you to raise your hand for what could be a truly career-defining opportunity.

Staff Developer Advocacy Engineer | Canada | Remote

This is a fully remote position and we're considering candidates in Canada.

The Opportunity:

Grafana is building the Interactive Learning Plugin: an open source, in-product learning experience inside Grafana that helps users level up without leaving their workflow. We’re hiring a software engineer embedded in Developer Advocacy who ships real product code that directly impacts both Grafana Cloud and Grafana OSS.

This is for an engineer who moves fast with strong judgment, is excited about AI-assisted development in open source, and wants to help build a platform where anyone can contribute high-quality learning content—not just engineers.

Your primary focus will be the open source Interactive Learning Plugin: https://github.com/grafana/grafana-pathfinder-app and its content: https://github.com/grafana/interactive-tutorials

What You’ll Be Doing:

Build delightful interactive learning inside Grafana

Ship features that make learning experiences feel obvious, smooth, and scalable.
Improve the end-to-end learner experience—from onboarding to completion.

Enable contribution and authoring (especially for non-engineers): create workflows and product features that let many contributors safely create, iterate on, and improve learning content.
Support private guides and experiences
Make completion tracking that actually matters

Build progress and completion tracking that’s trustworthy, motivating, and useful.
Instrument learning flows so we can understand what’s working, what’s failing, and where learners get stuck.

Treat observability as a product principle

Build fast feedback loops (metrics/logs/traces + user journey visibility) so issues stay shallow.
Make it easy to understand what’s happening in production and in real user experiences.

Ship fast with an AI-driven development workflow

Work daily with tools like Claude Code or Cursor in an agent-based workflow.
Apply strong engineering judgment—scoping, iterating, verifying, and testing—so speed doesn’t compromise quality.

We invest heavily in developer productivity.

Skills & Tags

typescript go rust kubernetes ai frontend backend devops

Aplyr's read

Grafana Labs is a leader in open-source analytics, attracting remote tech talent focused on data visualization and real-time monitoring solutions.
Synthesized from recent postings & public sources

What's promising

•Strong focus on open-source projects fosters innovation and community engagement.
•Remote work flexibility attracts a diverse, global talent pool.
•Rapid growth in AI and automation roles indicates forward-thinking strategy.

What to watch

•Highly competitive market with numerous analytics and monitoring tools.
•Dependency on open-source contributions can pose sustainability challenges.
•Remote work culture may lead to communication and collaboration hurdles.

Why Grafana Labs

•Grafana's open-source model allows for extensive customization and integration.
•The platform supports a wide range of data sources, enhancing versatility.
•Strong emphasis on real-time data visualization sets it apart from competitors.

Aplyr’s read is generated by AI from public sources. Was it useful?