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 system will support context ingestion, storage, retrieval, and agent-facing integrations.
This is an early-stage, high-autonomy role focused on building production services for the context layer management system. You will help develop systems that evolve from internal dogfooding to production-grade SaaS.
Responsibilities
- Design, implement, test, and operate backend services for context ingestion, indexing, retrieval orchestration, API access, source configuration, and system administration.
- Help define and build a scalable, multi-tenant SaaS architecture, 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.
- Make practical tradeoffs between rapid experimentation and 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 understand system behavior and improve reliability.
- Contribute to architecture, service boundaries, storage choices, API contracts, deployment patterns, and engineering practices for a new product area.
- Communicate effectively and collaborate across teams in a dynamic environment.
- Take ownership of AI solutions, ensuring they are scalable, maintainable, innovative, and aligned with real user workflows.
Requirements
- Strong experience building production-grade, user-facing software systems.
- Ability to work independently on complex engineering problems and make pragmatic architectural decisions with minimal supervision.
- Familiarity with AI technologies and frameworks, with a practical focus on delivering high-quality real-world solutions.
- Experience with rapid prototyping, experimentation, feedback collection, and iteration.
- Proven initiative and ownership, including the ability to work through ambiguity and define scope for loosely defined projects.
- Effective communication and a collaborative, solutions-oriented approach.
- Experience with LLMs, prompt engineering, and building applications powered by generative AI.
- Proven 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 building or working 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, remote-only company. The team meets regularly over video and does most of its work asynchronously and in writing. The role is open to applicants located in Spain, Sweden, the United Kingdom, Ireland, or Germany.
Compensation And Benefits
- Spain base compensation range: EUR 94,025–112,830 per year.
- Restricted Stock Units (RSUs) are included with all roles.
- Global annual leave policy of 30 days per year, including three Grafana Shutdown Days, subject to local legislation.
- In-person onboarding.
- Career growth opportunities, transparent communication, and an innovation-driven culture.