Your Dashboards Are a Museum of Dead Metrics. Fire the Curator.
In theory, Grafana is the window into the soul of your systems. It promises to take the torrent of raw metrics from tools like Prometheus, Loki, and Tempo and transform it into beautiful, intuitive, and actionable dashboards. It is supposed to be the command center for your entire engineering organization, a place where developers, SREs, and executives can go to get a clear, real-time understanding of system health.
In reality, for most organizations, the Grafana instance is a digital graveyard. It is a sprawling collection of hundreds of disconnected, untitled, and unmaintained dashboards. It is a gallery of "CPU Usage" graphs that tell you nothing about user experience. It is a place where every engineer creates their own personal dashboard, and no two dashboards agree on the definition of "error rate." Your observability platform, which was meant to be a source of truth, has become a source of confusion, distrust, and operational friction.
An engineer who can create a basic time-series panel in Grafana is not an observability expert. An expert understands that a dashboard is a narrative. They can design a layout that guides the viewer's eye from high-level SLOs down to specific, correlated metrics. They can write complex, multi-variable queries to join data from different sources. They use template variables to create dynamic, reusable dashboards that can be applied to hundreds of services. They treat their dashboards as a product, with version control, peer review, and a clear audience in mind. This playbook explains how Axiom Cortex finds the engineers who possess this rare and critical skill of data storytelling.
Traditional Vetting and Vendor Limitations
A nearshore vendor sees "Grafana" on a résumé, usually next to "Prometheus," and declares the candidate a senior observability engineer. The interview might involve asking them to describe what a dashboard is. This process is like judging an author by asking them if they know what a book is. It finds people who are aware of the tool. It completely fails to find people who have the skill to use it effectively.
The predictable and painful results of this superficial vetting become apparent across your organization:
- Dashboard Sprawl and Anarchy: There are 50 dashboards named "Untitled," "Test Dashboard," and "John's API Dashboard." When an incident occurs, no one knows which one to look at. The on-call engineer wastes the first 15 critical minutes of an outage just trying to find a graph that shows the error rate.
- The "Wall of Graphs": A dashboard consists of 30 different time-series panels with no clear organization, titles, or units. It is a visually overwhelming and cognitively useless display of data that provides no insight into the system's behavior.
- Query Inefficiency: Dashboards are painfully slow to load because they are powered by inefficient, brute-force queries that put an enormous strain on the underlying data source (like Prometheus or Loki).
- Lack of Context: A graph shows a spike in latency, but there is no context. There are no annotations to indicate when a new deployment happened. There are no links to jump to the relevant logs or traces. The dashboard shows the "what" but provides no help in finding the "why."
The business impact is a complete failure of your observability strategy. You have invested in powerful tools to collect terabytes of data, but you have no ability to turn that data into information, let alone wisdom. Your teams are flying blind, making decisions based on guesswork and intuition instead of data.
How Axiom Cortex Evaluates Grafana Developers
Axiom Cortex is designed to find the engineers who think like data journalists and systems thinkers, not just like chart-makers. We test for the practical skills in data modeling, query optimization, and information design that are essential for building a Grafana-based observability platform that actually works. We evaluate candidates across four critical dimensions.
Dimension 1: Information Architecture and Dashboard Design
A good dashboard tells a story. This dimension tests a candidate's ability to design a dashboard that is not just a collection of graphs, but a coherent and intuitive narrative about the health of a system.
We provide candidates with a set of raw metrics for a service and ask them to design a primary dashboard for the on-call team. We evaluate their ability to:
- Structure the Dashboard Logically: Do they organize the dashboard around the "golden signals" (latency, traffic, errors, saturation)? Do they place the most important, high-level information (like SLO status) at the top? Do they use rows to group related panels?
- Choose the Right Visualization: Can they explain why a time-series graph is appropriate for latency, but a stat panel or a gauge is better for a single-value metric like error percentage? Do they know when to use a table or a bar chart?
- Focus on Clarity and Readability: Do their panels have clear titles, correct units (ms, %, req/s), and sensible min/max ranges? Do they use color and thresholds to draw attention to important information?
Dimension 2: Advanced Querying and Templating
The real power of Grafana lies in its ability to execute complex queries and create dynamic, reusable dashboards. This dimension tests a candidate's mastery of these advanced features.
We present a complex scenario and evaluate if they can:
- Write Complex Queries: Can they write a query in PromQL (or another data source language) that involves aggregations, functions, and vector matching to produce the desired visualization?
- Use Template Variables: A high-scoring candidate will immediately suggest using template variables to create a single dashboard that can be used to view data for multiple services, environments, or regions. Can they configure variables that are populated by a query?
- Link Data Sources: Do they know how to link from a panel in one dashboard to another, passing the current context (like the selected time range and template variables)? Can they configure data links to jump from a graph to the relevant logs in Loki or traces in Jaeger?
Dimension 3: Grafana as a Platform (Administration and Automation)
In a professional environment, dashboards and alerts are code. They should be version-controlled, peer-reviewed, and deployed automatically. This dimension tests a candidate's ability to manage Grafana at scale.
We evaluate their knowledge of:
- Provisioning as Code: How would they manage their dashboards? A high-scoring candidate will talk about provisioning dashboards and data sources from version-controlled JSON or YAML files, not by clicking around in the UI.
- User and Team Management: Can they design a permissions model for Grafana that allows different teams to have their own folders and dashboards while still having access to shared, global dashboards?
- Alerting: Do they understand how to configure Grafana's alerting engine? Can they explain how to create multi-dimensional alerts and route them to different notification channels? They should also be able to articulate the pros and cons of alerting in Grafana vs. alerting directly in Prometheus Alertmanager.
Dimension 4: High-Stakes Communication and Collaboration
An observability platform serves many different audiences, from on-call engineers to product managers to executives. An elite observability engineer must be able to understand the needs of these different users and communicate with them effectively.
Axiom Cortex simulates real-world challenges to see how a candidate:
- Interviews a Stakeholder: We have them role-play an interview with a product manager to understand what information they need on their dashboard. We observe their ability to ask clarifying questions and translate business needs into technical requirements.
- Conducts a Dashboard Review: When reviewing a teammate's dashboard, do they provide constructive feedback on its clarity, usability, and technical implementation?
- Documents Their Work: Do they add clear descriptions to their dashboards and panels explaining what the data represents and how to interpret it?
From a Data Swamp to a Command Center
When you staff your observability team with engineers who have passed the Grafana Axiom Cortex assessment, you are making a strategic investment in the operational maturity of your entire company.
A Series B logistics company was struggling with frequent production incidents. They had a Grafana instance, but it was a chaotic mess of useless dashboards. When an outage occurred, it took the team an average of 45 minutes to diagnose the root cause because they couldn't find the right data. Using the Nearshore IT Co-Pilot, we assembled an "Observability" pod of two elite nearshore SREs.
In their first 90 days, this team:
- Created a "Golden Path" of Dashboards: They built a small set of standardized, templated dashboards for every microservice, focusing on the four golden signals and key business metrics.
- Implemented "Drill-Down" Capabilities: They configured data links so that an engineer could click on a spike in a latency graph and be taken directly to the slow query logs or distributed traces for that exact time window.
- Ran "Dashboarding Workshops": They taught the product development teams how to use the new standardized dashboards and how to contribute to them in a disciplined, version-controlled way.
The result was transformative. The company's Mean Time to Recovery (MTTR) for incidents dropped by over 70%. The development teams were able to identify and fix performance bottlenecks before they impacted customers. For the first time, the CTO had a single, trusted dashboard they could look at to see the real-time health of the entire business.
What This Changes for CTOs and CIOs
Using Axiom Cortex to hire for Grafana competency is not about finding someone who can make pretty charts. It is about insourcing the discipline of information design and data storytelling. It is a strategic move to turn your monitoring data from a reactive, forensic tool into a proactive, decision-making engine.
It allows you to change the conversation with your executive team. Instead of talking about infrastructure in terms of cost and uptime, you can talk about it in terms of performance, user experience, and business impact. You can say:
"We have built an observability platform with a nearshore team that has been scientifically vetted for their ability to translate raw system data into actionable business insights. This platform allows us to not only fix problems faster but also to make data-driven decisions about where to invest our engineering resources to have the greatest impact on our customers and our bottom line."
This is how you turn your observability stack from a cost center into a powerful engine of competitive advantage.