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 @ 6
Audit
Azure @ 6
BI @ 4
Communication @ 6
Data Engineering @ 4
Databricks @ 4
Experimentation @ 4
GCP @ 6
GenAI
Generative AI @ 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 and shipping backend architecture that supports ingestion, context storage, retrieval APIs, and agent-facing integrations. The role is remote and open to applicants located in Spain, Sweden, the United Kingdom, Ireland, or Germany.
Responsibilities
- Design, implement, test, and operate backend services for context ingestion, context indexing, retrieval orchestration, API access, source configuration, and system administration.
- Define and build a scalable, multi-tenant SaaS foundation, including tenant isolation, usage tracking, quotas, audit logs, background jobs, and reliable service boundaries.
- Build APIs and service interfaces that enable AI agents, MCP tools, CLIs, and internal applications to retrieve relevant context, provenance, confidence signals, and warnings.
- Balance rapid experimentation with long-term reliability as the project evolves from prototype to production.
- Instrument services with metrics, logs, traces, alerts, and dashboards, and use observability tools to understand system behavior and improve reliability.
- Contribute to technical direction, including architecture, service boundaries, storage choices, API contracts, deployment patterns, and engineering practices.
- Communicate effectively and contribute across teams in a dynamic, collaborative environment.
- Take ownership of AI solutions and ensure 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, make pragmatic architectural decisions, and work independently with minimal supervision.
- Familiarity with AI technologies and frameworks, with a practical focus on delivering high-quality solutions.
- Experience with quick iteration, prototyping, collecting feedback, and experimentation.
- Proven initiative, ownership, and ability to define scope and make decisions in ambiguous situations.
- Effective communication and a collaborative, solutions-oriented mindset.
- Experience with large language models, prompt engineering, and applications powered by generative AI.
- A track record of delivering software that reached production and is actively used by customers or users.
- Exposure to 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
Grafana Labs is a remote-first company. The team meets regularly over video and does most work asynchronously and in writing. Engineers are expected to contribute to technical direction, reliability, and product development.
Compensation and Benefits
- Germany base compensation range: EUR 109,709–131,651 per year.
- Benefits include equity, bonus where applicable, and other company benefits.
- Remote-first global culture.
- In-person onboarding.
- Global annual leave policy of 30 days per year, including three Grafana Shutdown Days, subject to local legislation.
- Career growth opportunities and an open-source, innovation-driven work environment.