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
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 seeking 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 requires comfort with ambiguity, pragmatic architectural decision-making, and evolving systems 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.
- Make practical tradeoffs between fast 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.
- Shape architecture, service boundaries, storage choices, API contracts, deployment patterns, and engineering practices for a new product area.
- 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 and make decisions with minimal supervision.
- Familiarity with AI technologies and frameworks, with a practical focus on delivering reliable real-world solutions.
- Experience with rapid prototyping, experimentation, feedback collection, and iterative development.
- Proven initiative and ownership, including the ability to define scope and work through ambiguity.
- Effective communication and a collaborative, solutions-oriented approach.
- Experience with LLMs, prompt engineering, and applications powered by generative AI.
- A proven track record of delivering software to production that 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
Grafana Labs is a remote-first company. The team meets regularly over video and does most work asynchronously and in writing. The role is open to applicants located in Spain, Sweden, the United Kingdom, Ireland, or Germany.
Compensation And Benefits
- Republic of Ireland base compensation: EUR 117,600–EUR 141,120 per year.
- Equity, bonus if applicable, and other benefits.
- 100% remote global culture.
- Career growth pathways.
- In-person onboarding.
- Global annual leave policy of 30 days per year, including three Grafana Shutdown Days, subject to local legislation.
Grafana Labs is an equal opportunities employer.