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Grafana Labs, the company behind the open observability cloud, is founded on the principles of open source, open standards, open ecosystems, and open culture. Grafana Cloud, our fully managed observability platform, is flexible and built for scale. With Grafana Cloud's actually useful AI, organizations can see, understand, and act on all their disparate data to move at the speed of their ambitions. Today, more than 35 million users and 7,000+ customers – including Anthropic, Bloomberg, NVIDIA, Microsoft, and Salesforce – trust Grafana Labs to ensure reliability of their applications and systems, resolve incidents quickly, and optimize their telemetry to reduce noise and cost. We are a 100% remote company with 1,600+ team members across 40+ countries, and we’re 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 and follow us on LinkedIn and X. 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. Staff Software Engineer - Grafana Databases, Tempo The Opportunity We build Tempo , the open-source distributed tracing backend behind Grafana Cloud Traces and Grafana Enterprise Traces (GET). Tempo makes it easy to search traces, generate metrics from spans, and connect tracing data with logs, metrics, and profiles across the Grafana stack. 2026 is an inflection point for Tempo. After a major architectural upgrade and the launch of TraceQL metrics, we are shifting from foundational work to product and operational excellence, and evolving Tempo from a SaaS database into a platform that powers Grafana’s next generation of observability products (App Observability, Asserts, Traces Drilldown, and AI-driven assistants). Over The Next Year, You Will Help Us Make Grafana Cloud Traces “just work” for customers by eliminating rough edges, confusing limits, and hidden failure modes. Achieve operational excellence at scale as we grow from close to 50 cells today into triple digits this year, with autoscaling, parameterized rollouts, and aggressive toil reduction. Evolve Tempo into a platform enabler: higher-density APIs, trace aggregation, TraceQL metrics math, and machine/LLM-friendly interfaces that downstream products and agents can build on. Push performance further: faster query latency at hundreds of MB/s ingestion and performant 30-day query ranges to match competitors. Prepare Tempo for an agent-driven world: larger, burstier, higher-cardinality workloads, and new categories of AI-powered workflows, such as assistant-driven triage and “why is this slow?”- style investigations. What You'll Be Doing As a Staff Engineer on Tempo, you will set technical direction on the hardest problems in our roadmap and raise the bar across the team. Lead multi-quarter technical initiatives from problem framing through rollout, e.g., trace aggregation APIs, Limitless Tempo, autoscaling cells and customer limits, or query engine improvements. Own the architecture of core Tempo components: ingestion, storage, query, and metrics generation. Drive design reviews, make sharp trade-offs on performance, cost, and complexity, and document the “why” for the team. Design APIs for humans and agents. Shape the next generation of Tempo’s interfaces (structured, deterministic, discoverable) so that Act 3 products, LLM-driven assistants, and external integrators can build on Tempo reliably. Drive operational excellence. Own outcomes against concrete SLOs (P99 write latency, incident recurrence, TCO per ingested GB) and push the team toward Zero Ops through automation, parameterized rollouts, and actionable alerts. Partner with Product and sibling teams. Work closely with PMs and with App Observability, Asserts, Drilldown, and Grafana Assistant teams to understand how Tempo gets consumed and to ship what unblocks them. Mentor engineers. Raise the engineering bar through code review, design feedback, pairing on hard problems, and writing that leaves the team smarter than you found it. Participate in on-call for the services you help build, and be a force multiplier in incident response and post-incident learning. Contribute to open source. Tempo is OSS. You will engage the community, review external contributions, and help steer the project in the open. We invest heavily in developer productivity. You can use modern AI coding assistants as part of your daily workflow (your choice of tools, within security guidelines), backed by a company-funded usage budget so you can iterate quickly without unnecessary friction. We encourage pragmatic AI-assisted development: faster prototyping, test generation, refactors, documentation, and incident follow-ups—always paired with strong code review and quality standards. You’ll also have access to frontier models (e.g., GPT-5.6, Claude Fable, Gemini Pro 3.1). Example Problems You Could Work On These are the kinds of projects landing in 2026. Any one of them is a Staff-sized problem: Trace aggregation and higher-density APIs: extend TraceQL metrics, design LLM-friendly response types, and make Tempo a first-class data source for Grafana’s AI assistant. Autoscaling end to end: customer limits and Tempo cells, with hysteresis, predictive scaling for spikes, and safe scale-down. Agent-scale ingestion and query: guardrails for bursty, high-cardinality, agent-generated workloads. Query performance: new data formats, smarter query pipelines, targeted optimizations for common Drilldown and Traces workflows, and 30-day query ranges. Rollouts and multi-cell operations: parameterized rollouts, push-button deploys, and the tooling to grow safely into triple-digit cell counts without a proportional increase in alert noise. Limits and self-service: drive customer-facing configuration and observability so escalations trend toward zero. What Makes You a Great Fit Technical leadership. A track record of leading complex, multi-quarter initiatives that spanned design, delivery, and operations, and made the teams around you better. Deep systems experience. Substantial hands-on experience building and operating distributed data systems in production: ingestion pipelines, storage engines, query execution, or similar. Strong software craftsmanship. You write clean, robust, performant software that others can maintain, and you know when to optimize vs. when to ship. Strong Go, or a path to it. We write Tempo in Go. Deep experience in other systems languages (Rust, C, C++) translates well. Operational mindset. You’ve owned production services, carried a pager, reduced toil, and treated SLOs as a product feature, not a chore. Customer focus and pragmatism. You break complex problems into short feedback loops: analyze, design, deliver an MVP, learn, iterate. Leadership through writing and collaboration. You lead through design docs, reviews, and shipped code, not hierarchy. You communicate clearly in a fully remote, asynchronous environment. Bonus Points For Experience with tracing, OpenTelemetry, or large-scale observability systems. Experience designing query languages, SQL/TraceQL-like engines, or APIs intended to be consumed programmatically (by services or agents). Experience with columnar storage formats (e.g., Parquet) or purpose-built on-disk formats for analytical workloads. Experience operating multi-tenant, multi-cell SaaS infrastructure at scale on Kubernetes. Experience building for AI/LLM consumers:
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.