[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 | Germany | Remote AI Summary Grafana Labs is seeking a senior backend-focused full-stack engineer to build production services for an AI-native enterprise data intelligence platform. The role covers context ingestion, indexing, storage, retrieval APIs, multi-tenant SaaS architecture, and integrations used by AI agents and internal tools. The engineer will own systems from early experimentation through reliable production operation, including observability, quotas, audit logs, and background jobs. Success requires practical GenAI experience, cloud-native engineering skills, strong architectural judgment, and effective asynchronous collaboration. The role is remote for candidates located in Spain, Sweden, the UK, Ireland, or Germany, with in-person onboarding.[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)SalaryEUR 100k–125k / yrDepartment[Software Engineering](https://jobicy.com/categories/engineering.md)EmploymentFull TimeExperienceSeniorPublished14 Nov 2026Apply before14 Dec 2026Listing views36Application actions4Application 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 Germany, the Base compensation range for this role is EUR 100,000 – EUR 125,000. 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/162101-senior-full-stack-engineer-databases-analytics-germany-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 4/5IndependentCollaborative AI insightThis is a staff-level, early-stage engineering role requiring architecture ownership across AI retrieval, multi-tenant SaaS, and production reliability. The successful candidate must make pragmatic decisions under ambiguity while rapidly evolving a prototype into a scalable service. ## Salary analysis Estimated compensation compared with the broader EU market for similar roles. Estimated job median (per year)Market rate€112,500EU market range (per year)€105k–€145k0€159.50k AI insightThe yearly pay period is inferred from the job context; it was not explicitly stated by the employer. The employer discloses a Germany-specific base compensation range of EUR 100,000 to EUR 125,000. The annual cadence is inferred from the professional seniority, full-time context, and the magnitude of the stated base-pay range; the posting does not explicitly state a pay period. The midpoint is EUR 112,500 per year. A reasonable estimated market range for a staff-level backend engineer with AI and cloud expertise in Germany is EUR 105,000 to EUR 145,000 per year. ## Core skills Skills and capabilities most closely associated with this opportunity. [Backend architecture](https://jobicy.com/jobs?search_keywords=Backend%20architecture.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)[AI agent integrations](https://jobicy.com/jobs?search_keywords=AI%20agent%20integrations.md)[LLM applications](https://jobicy.com/jobs?search_keywords=LLM%20applications.md)[Prompt engineering](https://jobicy.com/jobs?search_keywords=Prompt%20engineering.md)[Context retrieval](https://jobicy.com/jobs?search_keywords=Context%20retrieval.md)[Cloud-native development](https://jobicy.com/jobs?search_keywords=Cloud-native%20development.md)[AWS](https://jobicy.com/jobs?search_keywords=AWS.md)[GCP](https://jobicy.com/jobs?search_keywords=GCP.md)[Azure](https://jobicy.com/jobs?search_keywords=Azure.md)[Observability](https://jobicy.com/jobs?search_keywords=Observability.md)[System reliability](https://jobicy.com/jobs?search_keywords=System%20reliability.md)[Technical leadership](https://jobicy.com/jobs?search_keywords=Technical%20leadership.md) Sample interview questionsHow would you design a multi-tenant context retrieval service that provides strong tenant isolation and low-latency access?I would establish tenant identity and authorization at the API boundary, propagate it through every request and background job, and enforce it at both application and storage layers. I would choose data partitioning and indexing strategies based on scale and isolation requirements, add quotas and rate limits, and make audit events immutable and queryable. I would also define latency SLOs, instrument retrieval paths with traces and metrics, and validate isolation through automated security and integration tests. How do you balance rapid experimentation with building a service that can become production-grade? I start by identifying the decisions that are costly to reverse, such as tenancy boundaries, API contracts, data ownership, and security controls. I keep other areas deliberately lightweight behind clear interfaces, release a narrow end-to-end slice early, and use usage data and customer feedback to guide iteration. Production readiness is built incrementally through observability, automated testing, operational runbooks, and explicit reliability milestones. Describe how you would make retrieval results from enterprise data useful and trustworthy for AI agents. I would return more than raw content by including source provenance, timestamps, access-control context, confidence indicators, and warnings about freshness or incomplete coverage. Retrieval should be governed by the same permissions as the source systems, with deterministic filtering before ranking. I would evaluate quality using representative agent tasks, measure relevance and failure modes, and provide feedback mechanisms to improve indexing and ranking over time. What observability would you implement for a new ingestion and retrieval platform? I would define service-level indicators for availability, latency, error rate, ingestion freshness, queue depth, retrieval quality proxies, and tenant-level usage. Structured logs, distributed traces, and metrics would connect ingestion events to indexing and API responses. Dashboards and alerts would focus on user impact, while audit logs and sampled request records would support debugging, governance, and incident investigation. How do you communicate technical tradeoffs in a distributed, asynchronous team? I write concise decision documents that describe the problem, constraints, options, recommendation, risks, and reversible versus irreversible choices. I invite targeted feedback from engineering, product, and infrastructure partners early, then record the decision and ownership clearly. After implementation, I share outcomes and operational learnings so that the team can refine future decisions. Next step ## Apply now. Follow the employer’s application method and review Jobicy’s safety guidance before sharing personal information. 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