Tech Stack
Tag name is followed by "@" symbol and proficiency level value.
About proficiency levels:
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
AI @ 4
API
AWS @ 4
Audit
Azure @ 4
BI @ 4
Communication @ 6
Data Engineering @ 4
Databricks @ 4
Experimentation @ 4
GCP @ 4
GenAI @ 4
Grafana @ 4
LLM
Looker @ 4
Observability @ 4
Power BI @ 4
Prompt Engineering @ 4
Snowflake @ 4
System Administration
Tableau @ 4
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Details
Grafana Labs is building an AI-native data intelligence system that gives agents reliable, governed access to enterprise context, including data, metadata, definitions, lineage, quality signals, and institutional knowledge. The team is looking for a Staff-level Backend Engineer to build the first production services for this context layer management system.
This is an early-stage, high-autonomy role focused on designing backend architecture that can evolve from internal dogfooding to a production-grade, multi-tenant SaaS product.
Responsibilities
- Design, implement, test, and operate backend services for context ingestion, indexing, retrieval orchestration, API access, source configuration, and system administration.
- Define and build a scalable SaaS foundation, including tenant isolation, usage tracking, quotas, audit logs, background jobs, and reliable service boundaries.
- Build APIs and service interfaces for AI agents, MCP tools, CLIs, and internal applications to retrieve context, provenance, confidence signals, and warnings.
- Balance rapid experimentation with long-term reliability as the project moves from prototype to production.
- Instrument services with metrics, logs, traces, alerts, and dashboards, and use observability tools to improve reliability.
- Contribute to architecture, service boundaries, storage choices, API contracts, deployment patterns, and engineering practices.
- Communicate effectively and collaborate across teams.
- Take ownership of AI solutions, ensuring they are scalable, maintainable, and aligned with real user workflows.
Requirements
- Strong experience building production-grade, user-facing software systems.
- Ability to tackle complex engineering problems and make decisions with minimal supervision.
- Familiarity with AI technologies and frameworks, with a practical focus on delivering reliable solutions.
- Experience with quick prototyping, experimentation, feedback collection, and iterative development.
- Proven initiative, ownership, and ability to define scope in ambiguous environments.
- Effective communication and a collaborative, solutions-oriented approach.
- Experience with LLMs, prompt engineering, and GenAI-powered applications.
- A proven track record of delivering software that reached production and is actively used by customers or users.
- Experience working in cloud-native environments such as AWS, GCP, or Azure.
- Experience using observability tools to understand and troubleshoot system behavior.
Bonus Points
- Experience with agent frameworks or multi-agent workflows.
- Experience as a data analyst or working with data platforms such as Looker, Tableau, Power BI, Snowflake, or Databricks.
- Experience building tools for data engineering.
Work Environment
This is a remote opportunity for applicants located in Spain, Sweden, the United Kingdom, Ireland, or Germany. Grafana Labs is a remote-first company that works primarily asynchronously and in writing, with regular video meetings.
Compensation And Benefits
The Germany base compensation range is EUR 109,709–131,651 per year. Actual compensation may vary based on level, experience, and skills. Benefits include equity, a potential bonus, 30 days of annual leave per year, and in-person onboarding. The company also offers a remote-first global culture, career growth pathways, and an open-source-oriented working environment.