[All remote jobs](https://jobicy.com/jobs.md)[![Grafana Labs logo](https://jobicy.com/data/server-nyc0409/galaxy/mercury/2021/11/42a3c42246467ec231dd876e92b63452.jpeg)](https://jobicy.com/company/grafana-labs.md)[Grafana Labs](https://jobicy.com/company/grafana-labs.md) # Senior Full Stack Engineer – Databases – Analytics | UK | Remote AI Summary Grafana Labs is hiring a senior backend-focused full-stack engineer to build an AI-native context management platform for enterprise data and agent workflows. The role owns production services for ingestion, indexing, retrieval, APIs, multi-tenant controls, auditability, and operational reliability. It requires strong judgment in ambiguous early-stage product work, practical GenAI experience, and the ability to evolve prototypes into dependable SaaS systems. The engineer will work asynchronously with a remote team and influence architecture, service boundaries, storage decisions, and engineering practices. Candidates must be located in Spain, Sweden, the United Kingdom, Ireland, or Germany, with in-person onboarding required.[Apply for this job](#job-application)[View company](https://jobicy.com/company/grafana-labs.md)ShareRemote from[UK](https://jobicy.com/job-region/uk.md), [Spain](https://jobicy.com/job-region/spain.md), [Germany](https://jobicy.com/job-region/germany.md)+2 more, [Sweden](https://jobicy.com/job-region/sweden.md), [Ireland](https://jobicy.com/job-region/ireland.md)SalaryGBP 103,958–124,750 / yrDepartment[Software Engineering](https://jobicy.com/categories/engineering.md)EmploymentFull TimeExperienceSeniorPublished14 Nov 2026Apply before14 Dec 2026Listing views15Application actions1Application toolkit ## Make your next move. Prepare your resume, explore your fit, and draft a cover letter for this opportunity. Opportunity details ## About this role. [Grafana Labs](https://jobicy.com/company/grafana-labs.md) is the company behind Grafana Cloud, the fully managed observability platform trusted by more than 10,000 organizations to ensure reliability, resolve incidents faster, and optimize telemetry at scale. Built on open source and open standards and designed for interoperability across any stack, Grafana Cloud brings AI to observability and observability to AI, giving teams (and their agents) unified visibility so they can see, understand, and act on all their disparate data, wherever it lives, and move at the speed of their ambitions. Customers, including Anthropic, Bloomberg, NVIDIA, Microsoft, and Salesforce, rely on Grafana Labs. We are 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. Learn more at [grafana.com](http://grafana.com) and follow us on [LinkedIn](https://www.linkedin.com/company/grafana-labs/) and [X](https://twitter.com/grafana). We’re scaling fast and staying true to what makes us different: an open-source legacy, a global collaborative culture, and a passion for meaningful work. Our team thrives in an innovation-driven environment where transparency, autonomy, and trust fuel everything we do. You may not meet every requirement, and that’s okay. If this role excites you, we’d love you to raise your hand for what could be a truly career-defining opportunity. This is a remote opportunity, and we would be interested in applicants located in Spain, Sweden, UK, Ireland or Germany. ### The Opportunity: At Grafana Labs, we build observability tools that help users understand, respond to, and improve their systems – regardless of scale, complexity, or tech stack. We recently started a skunkworks initiative with a mission to bring observability to the rest of the business. Our goal is to make Grafana the single best place where humans and AI agents understand and act on real-time data from across the enterprise. As part of this initiative, we are building an AI-native data intelligence system that gives agents reliable, governed access to enterprise context. This system helps agents retrieve the right data, metadata, definitions, lineage, quality signals, and institutional knowledge without every agent builder having to maintain their own brittle context files. The principle is simple: the context system retrieves, agents decide, and data teams maintain the intelligence. The new team is a mix of seasoned Grafanistas and new hires. We operate with a high degree of autonomy and ownership, both as individuals and as a team. Engineers are empowered to make decisions, move quickly, and validate ideas early – while being supported by a deeply collaborative culture that values curiosity, feedback, and cross-functional partnership. We’re looking for a Staff-level Backend Engineer to help build the first production services for this context layer management system. You will design and ship the core backend architecture that powers ingestion, context storage, retrieval APIs, and agent-facing integrations. This is an early-stage role on a high-autonomy team, so you should be comfortable working through ambiguity, making pragmatic architectural decisions, and building systems that can evolve from internal dogfooding to production-grade SaaS. As the team matures, there’s a broad opportunity to expand or redefine this role based on impact and initiative. ### What You’ll Be Doing: * Build the core backend services: Design, implement, test, and operate the first services for context ingestion, context indexing, retrieval orchestration, API access, source configuration, and system administration. * Create a scalable SaaS foundation: Help define and build the architecture for a multi-tenant service, including tenant isolation, usage tracking, quotas, audit logs, background jobs, and reliable service boundaries. * Power agent-facing retrieval workflows: Build APIs and service interfaces that allow AI agents, MCP tools, CLIs, and internal applications to retrieve relevant context, provenance, confidence signals, and warnings. * Work across product and infrastructure: Partner with the team to make practical tradeoffs between fast experimentation and long-term reliability, especially as the project moves from prototype to production. * Operate what you build: Instrument services with metrics, logs, traces, alerts, and dashboards. Use observability tools to understand system behavior and improve reliability. * Contribute to technical direction: Help shape the architecture, service boundaries, storage choices, API contracts, deployment patterns, and engineering practices for a new product area. * Effective communication: You’ll be working in a highly dynamic and collaborative environment, so we need someone who can communicate effectively and contribute across teams. * Ownership and impact: Take full ownership of the AI solutions you develop, ensuring they are not only innovative but also scalable, maintainable, and aligned with real user workflows. ### What Makes You a Great Fit: * Strong engineering skills: Solid experience building production-grade, user-facing software systems. You’re a self-starter, capable of tackling complex engineering problems and making UI design decisions with minimal supervision. * AI experience with a practical mindset: You’re familiar with AI technologies and frameworks, and you focus on delivering high-quality solutions that work in the real world, not just in theory. * Quick iteration and experimentation: You’re comfortable releasing prototypes, collecting feedback, and iterating with a pragmatic mindset. * Proven initiative: You take ownership and drive projects forward, pushing boundaries to find the most impactful solutions. You can deal with ambiguity and are able to define scope where things are loosely defined. * Collaborative attitude: You communicate effectively with your peers. You’re open to feedback, and you bring a solutions-oriented mindset to the table. * Experience with LLMs, prompt engineering, and building applications powered by GenAI. * Proven track record of delivering software that made it into production and is actively used by users. * Exposure to working in cloud-native environments (e.g., AWS, GCP, Azure). * Experience using observability tools to understand and troubleshoot system behavior. ### Bonus Points for: * Experience building or working with agent frameworks or multi‑agent workflows. * Experience as a data analyst or work with data platforms (e.g., Looker, Tableau, PowerBI, Snowflake, DataBricks) * Experience building tools for data engineering. How we work: We are a remote-first team that meets regularly over video and does most of our work asynchronously, in writing. We value creativity, diverse perspectives, and clear communication. Tempo is relied upon by prominent global organizations to monitor critical applications and infrastructure, and we expect everyone on the team, including our Staff engineers, to contribute ideas that make it a more reliable, more useful, and more loved product. Compensation & Rewards: In the United Kingdom, the Base compensation range for this role is GBP 103,958 – GBP 124,750. Actual compensation may vary based on level, experience, and skillset as assessed in the interview process. Benefits include equity, bonus (if applicable) and other benefits listed [here](https://grafana.com/about/careers/#jobs). *Compensation ranges are country specific. If you are applying for this role from a different location than listed above, your recruiter will discuss your specific market’s defined pay range & benefits at the beginning of the process *Compensation ranges are country specific. If you are applying for this role from a different location than listed above, your recruiter will discuss your specific market’s defined pay range & benefits at the beginning of the process. Why You’ll Thrive at Grafana Labs: * 100% Remote, Global Culture – As a remote-only company, we bring together talent from around the world, united by a culture of collaboration and shared purpose. * Scaling Organization – Tackle meaningful work in a high-growth, ever-evolving environment. * Transparent Communication – Expect open decision-making and regular company-wide updates. * Innovation-Driven – Autonomy and support to ship great work and try new things. * Open Source Roots – Built on community-driven values that shape how we work. * Empowered Teams – High trust, low ego culture that values outcomes over optics. * Career Growth Pathways – Defined opportunities to grow and develop your career. * Approachable Leadership – Transparent execs who are involved, visible, and human. * Passionate People – Join a team of smart, supportive folks who care deeply about what they do. * In-Person onboarding – We want you to thrive from day 1 with your fellow new ‘Grafanistas’ to learn all about what we do and how we do it. * Balance is Key – We operate a global annual leave policy of 30 days per annum. 3 days of your annual leave entitlement are reserved for Grafana Shutdown Days to allow the team to really disconnect. *We will comply with local legislation where applicable. Equal Opportunity Employer: Grafana Labs is an equal opportunities employer. We welcome applications from everyone regardless of race, colour, nationality, origin, caste, sex, gender reassignment identity or expression, sexual orientation, age, religion or belief, disability, veteran status, genetic information, pregnancy, maternity, marital, family or carer status, or any other characteristic which is protected by local law. We believe that equality and diversity build a strong organisation, and we work hard to ensure that is the foundation of our organisation as we grow. Grafana Labs may utilize AI tools in its recruitment process to assist in matching information provided in CVs to job postings. The recruitment team will continue to review inbound CVs manually to identify alignment with current openings. #LI-Remote For information about how your personal data is used once you’ve applied to a job, check out our [privacy policy](https://grafana.com/legal/applicant-and-candidate-privacy-policy/). Show more [Apply now >](https://jobicy.com/jobs/162114-senior-full-stack-engineer-databases-analytics-uk-remote.md) * ![Upload CV](data:image/svg+xml;base64,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) ### Upload your resume now To unlock remote work opportunities and be discovered by global employers. This job listing has been manually reviewed by the Jobicy Trust & Safety Team for compliance with our posting guidelines, including verification of the company's legitimacy, accuracy of job details, clarity of remote work policy, and absence of misleading or fraudulent content. ## Role DNA A quick view of the complexity, pace, ownership and collaboration implied by the job description. ### Job Complexity 5/5EasyHard ### Pace & Pressure 5/5RelaxedFast-paced ### Autonomy Level 5/5GuidedFull ownership ### Communication Load 5/5IndependentCollaborative AI insightThis is a staff-level, greenfield engineering role combining distributed backend architecture, AI-agent integrations, multi-tenant SaaS design, and production operations. The candidate must make high-impact technical decisions amid significant ambiguity while moving quickly from experimentation to reliable delivery. ## Salary analysis Estimated compensation compared with the broader UK market for similar roles. Estimated job median (per year)Market rate£114,354UK market range (per year)£95k–£135k0£148.50k AI insightThe yearly pay period is inferred from the job context; it was not explicitly stated by the employer. The employer discloses a UK base compensation range of GBP 103,958 to GBP 124,750. The annual cadence is inferred from the scale of the stated base pay and UK staff-level full-time engineering market conventions, as the posting does not explicitly state a pay period. The midpoint is GBP 114,354 per year; a reasonable estimated UK market range for a comparable senior/staff backend engineering role is GBP 95,000 to GBP 135,000 per year. ## Core skills Skills and capabilities most closely associated with this opportunity. [Backend engineering](https://jobicy.com/jobs?search_keywords=Backend%20engineering.md)[Distributed systems](https://jobicy.com/jobs?search_keywords=Distributed%20systems.md)[Multi-tenant SaaS](https://jobicy.com/jobs?search_keywords=Multi-tenant%20SaaS.md)[API design](https://jobicy.com/jobs?search_keywords=API%20design.md)[Cloud-native architecture](https://jobicy.com/jobs?search_keywords=Cloud-native%20architecture.md)[LLM applications](https://jobicy.com/jobs?search_keywords=LLM%20applications.md)[Prompt engineering](https://jobicy.com/jobs?search_keywords=Prompt%20engineering.md)[Agent frameworks](https://jobicy.com/jobs?search_keywords=Agent%20frameworks.md)[Data ingestion](https://jobicy.com/jobs?search_keywords=Data%20ingestion.md)[Information retrieval](https://jobicy.com/jobs?search_keywords=Information%20retrieval.md)[Observability](https://jobicy.com/jobs?search_keywords=Observability.md)[OpenTelemetry](https://jobicy.com/jobs?search_keywords=OpenTelemetry.md)[AWS](https://jobicy.com/jobs?search_keywords=AWS.md)[System design](https://jobicy.com/jobs?search_keywords=System%20design.md)[Technical leadership](https://jobicy.com/jobs?search_keywords=Technical%20leadership.md) Sample interview questionsHow would you design a multi-tenant context ingestion and retrieval platform for AI agents?I would separate ingestion, indexing, retrieval, and administration into well-defined services with explicit contracts. Tenant identity and authorization would be enforced at every boundary, while metadata, lineage, quotas, and audit events would be first-class entities. I would begin with a narrow, observable architecture that can support early users, then evolve storage and retrieval layers based on measured scale and relevance requirements. How do you balance rapid experimentation with production reliability in an early-stage product? I define the smallest testable outcome and release it behind controlled access, feature flags, and clear telemetry. Even prototypes should include basic authentication, error handling, logs, metrics, and rollback paths. Once feedback validates the direction, I prioritize the reliability work that removes the biggest operational and user-facing risks. What observability signals would you implement for agent-facing retrieval APIs? I would track request volume, latency percentiles, error rates, retrieval quality proxies, token or compute usage, tenant-level quotas, and dependency health. Traces should link an agent request through authorization, retrieval, ranking, and downstream source calls. Structured logs and audit trails should capture provenance, policy decisions, warnings, and failures without exposing sensitive customer content. How would you evaluate whether a context retrieval system is improving AI-agent outcomes? I would establish representative tasks and evaluate retrieval precision, recall, provenance coverage, freshness, latency, and the downstream agent success rate. Offline test sets are useful for regression testing, but I would also collect production feedback signals such as result selection, corrections, abstentions, and task completion. Evaluation criteria should be tied to user workflows rather than only model-centric metrics. Describe how you make architecture decisions when requirements are ambiguous. I clarify the user problem, constraints, expected scale, and irreversible decisions first. I document a small number of options with their tradeoffs, choose the simplest approach that preserves future flexibility, and validate it through an implementation spike or limited release. I communicate the decision, assumptions, and review triggers so the team can revisit it as evidence changes. Next step ## Apply now. Follow the employer’s application method and review Jobicy’s safety guidance before sharing personal information. 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