Grafana Labs · 21 hours ago

Staff Software Engineer - Grafana Backend Services (Remote, USA)

United States

Full-time

Remote

Lead/Staff

$149K/yr - $178K/yr

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Comp. & Benefits

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Responsibilities

Work with your team to deliver new functionality, then use the results to iterate and improve

Contributing to and reviewing design documents for upcoming features

Embrace our open-source culture and contribute to other projects that may not directly fall within your team’s scope

Take an active role in influencing our roadmap and your career objectives

Be a part of your team’s on-call rotations and take ownership of the services you’re running

Mentor and support other team members, participate in design discussions, and collaborate with the team

Regularly sharing ideas and knowledge across a vast array of technical ideologies and mindsets

Working closely with the other squads in the Grafana Division

Qualification

Find out how your skills align with this job's requirements. If anything seems off, you can easily click on the tags to select or unselect skills to reflect your actual expertise.

GoLangMySQLSQLitePostgresDockerKubernetesCI/CDDevOpsMicroservicesDistributed SystemsOpen-source SoftwareGrafanaPrometheusMentoring

Required

Have solid experience with Go

Good understanding of storage solutions with an emphasis on relational databases like MySQL, SQLite, and Postgres

Care deeply about developer and user experience and the quality of the products that you work on

Have experience with delivering projects from gathering requirements, and brainstorming ideas to shipping a product to the customer’s hands in a self-driven way

Have experience writing clean, robust, and performant software that is easily maintained by others

Have experience with mentoring junior engineers in a collaborative but asynchronous environment

Enjoy working on complex solutions – Grafana is a highly technical solution and has avid followers who rely on it every day and care deeply about their workflows.

Can take on complex challenges and break them down to achieve tight learning loops: to analyze, design, and build modular solutions, deliver MVPs, gather data and feedback, and then progress iteratively