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
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 Staff Backend Engineer will help 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 involves working through ambiguity and building systems that can evolve from internal dogfooding to production-grade SaaS.
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.
- Partner across product and infrastructure to 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 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 contribute in a dynamic, collaborative, and cross-functional environment.
- Take ownership of AI solutions, ensuring they are innovative, scalable, maintainable, and aligned with real user workflows.
Requirements
- Staff-level experience and strong engineering skills building production-grade, user-facing software systems.
- Ability to work independently, tackle complex engineering problems, and make pragmatic decisions with minimal supervision.
- Familiarity with AI technologies and frameworks, with a practical focus on delivering high-quality solutions for real-world use.
- Comfort releasing prototypes, collecting feedback, and iterating quickly.
- Proven initiative and ownership, including the ability to define scope in ambiguous environments and drive projects forward.
- Effective communication, openness to feedback, and a solutions-oriented mindset.
- Experience with LLMs, prompt engineering, and building applications powered by generative AI.
- A 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 company values creativity, diverse perspectives, clear communication, autonomy, transparency, and collaboration.
Compensation And Benefits
The United Kingdom base compensation range is GBP 103,958–124,750. Actual compensation may vary based on level, experience, and skillset. Benefits include equity, bonus where applicable, and other benefits. The company offers a global annual leave policy of 30 days per year, including three Grafana Shutdown Days. In-person onboarding is provided, subject to local legislation.
Grafana Labs is an equal opportunities employer and welcomes applications from people with diverse backgrounds and characteristics protected by local law.