SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
Salary: GBP 91,000 - 114,700 / annual
Grafana Labs is building an AI-native data intelligence system as part of a new skunkworks initiative to bring observability to the broader enterprise. The mission is to make Grafana the single best place where humans and AI agents understand and act on real-time data across the enterprise. This system provides agents with reliable, governed access to enterprise context—retrieving data, metadata, definitions, lineage, quality signals, and institutional knowledge without requiring individual agent builders to maintain brittle context files.
You will join a high-autonomy team of seasoned Grafanistas and new hires, operating with significant ownership and empowerment to make decisions, move quickly, and validate ideas early. The team values curiosity, feedback, and cross-functional partnership.
As a Senior Backend Engineer focused on Databases, you will own and build one or multiple parts of the database layer, which supports both OLAP and OTLP capabilities for classic analytics queries and freshly stored data. The system embraces separation of compute and storage and is designed to run on multiple cloud providers. This is an early-stage role where you'll work through ambiguity, make pragmatic architectural decisions, and build systems that evolve from internal dogfooding to production-grade SaaS.
Key responsibilities include:
- Design, implement, test, and operate ingestion, query planning, distributed query execution, data formats, and storage formats
- Define and build architecture for a multi-tenant database service, including tenant isolation, usage tracking, quotas, audit logs, background jobs, and reliable service boundaries
- Build APIs and database interfaces for AI agents, MCP tools, CLIs, and internal applications to retrieve data quickly
- Partner across product and infrastructure teams to balance fast experimentation with long-term reliability
- Instrument database services with metrics, logs, traces, alerts, and dashboards; use observability tools to understand system behavior and improve reliability
- Help shape architecture, service boundaries, storage choices, API contracts, deployment patterns, and engineering practices for a new product area
- Communicate effectively in a dynamic, collaborative environment
- Take full ownership of database solutions, ensuring they are innovative, scalable, maintainable, and aligned with real user workflows
Grafana Labs is a 100% remote company with team members across 40+ countries, backed by leading investors including Lightspeed Venture Partners, Sequoia Capital, GIC, Coatue, J.P. Morgan, CapitalG, and Lead Edge Capital. Customers include Anthropic, Bloomberg, NVIDIA, Microsoft, and Salesforce.
REQUIREMENTS:
- Strong engineering skills: solid experience building production-grade, user-facing software systems; self-starter capable of tackling complex engineering problems and making design decisions with minimal supervision
- AI experience with a practical mindset: familiar with AI technologies and frameworks, focused on delivering high-quality solutions that work in the real world
- Quick iteration and experimentation: comfortable releasing prototypes, collecting feedback, and iterating pragmatically
- Proven initiative: take ownership and drive projects forward; can deal with ambiguity and define scope where loosely defined
- Collaborative attitude: communicate effectively with peers; open to feedback; solutions-oriented mindset
- Experience with distributed systems, catalogs and table formats, query engines
- Mastery of a programming language like Go, C++, or Rust
- Proven track record of delivering software that made it into production and is actively used by users
- Exposure to cloud-native environments (AWS, GCP, Azure)
- Experience using observability tools to understand and troubleshoot system behavior
BONUS:
- Experience building distributed query engines
- Experience building data warehouses and/or data lakes
- Experience building tools for data engineering