Why Observability Skills Are the New Must-Have for Engineers

Introduction: The Observability Gap

Writing code that works on your laptop is easy. Understanding why that same code behaves unexpectedly in production—at scale, under load, with real users—is an entirely different skill. That skill is observability, and it's becoming one of the most valuable capabilities in modern engineering.

Yet most hiring processes ignore it completely. We test whether candidates can implement algorithms from memory while ignoring whether they can read a log file, understand a dashboard, or correlate events across distributed systems.

"Our best incident responders aren't our best coders—they're the engineers who can read between the lines of logs and metrics to find the needle in the haystack."

— SRE Lead, Major Fintech Company

The Three Pillars of Observability

Observability rests on three fundamental pillars, each serving a distinct purpose:

Logs: The Story of What Happened

Logs are discrete events recorded by your system. They tell you what happened and when. Good logging practices include structured logging, appropriate log levels, and meaningful context.

• Request received, processed, completed
• Errors and exceptions with stack traces
• Business events and state transitions
• Security events and audit trails

Metrics: The Numbers That Matter

Metrics are numerical measurements over time. They answer questions like how many, how fast, and how often. The four golden signals are latency, traffic, errors, and saturation.

• Request latency percentiles (p50, p95, p99)
• Error rates and success rates
• Throughput and capacity utilization
• Business metrics (orders, revenue, signups)

Traces: The Journey of a Request

Distributed traces follow a request through multiple services. They show you the complete picture of where time is spent and how services interact.

• End-to-end request paths
• Service dependencies and call graphs
• Latency breakdown by component
• Error propagation across services

Why Observability Skills Matter Now

Several trends make observability skills more critical than ever:

Increasing System Complexity

Microservices, serverless, and distributed systems create complexity that's impossible to understand by reading code alone. You need runtime visibility.

AI-Generated Code

As teams generate more code with AI assistance, the code is often less familiar. Engineers need observability to understand how AI-generated code behaves in production.

Speed Requirements

Modern deployment practices (continuous delivery, feature flags) mean code reaches production faster. Quick detection and diagnosis of problems is essential.

Customer Expectations

Users expect 99.9%+ uptime. Meeting these expectations requires proactive monitoring and rapid incident response—both dependent on observability.

Core Observability Skills to Look For

When hiring, assess these specific observability competencies:

Log Analysis

• Can they construct effective log queries?
• Do they understand log levels and when to use each?
• Can they correlate events across multiple log sources?
• Do they know how to add useful logging to code?

Metrics Interpretation

• Can they read and interpret dashboards?
• Do they understand percentiles and averages?
• Can they identify anomalies in time-series data?
• Do they know which metrics matter for different scenarios?

Distributed Tracing

• Can they follow a request through multiple services?
• Do they understand propagation context?
• Can they identify bottlenecks in traces?

Instrumentation

• Do they know how to add observability to code?
• Can they design meaningful alerts?
• Do they think proactively about monitoring?

Assessing Observability in Interviews

Here's how to evaluate observability skills during technical interviews:

Scenario-Based Questions

Present realistic production scenarios: "Users report the checkout process is slow. Here's the dashboard showing our service metrics. Walk me through how you'd investigate."

Log Analysis Exercise

Provide real (anonymized) log samples with a hidden issue. Can the candidate find the root cause through careful analysis?

Instrumentation Task

Give candidates a code snippet and ask them to add appropriate logging, metrics, or tracing. What do they choose to measure and why?

Questions to Ask

• "Tell me about a production incident you helped debug"
• "How do you decide what to log and at what level?"
• "What's the difference between good and bad alerts?"
• "How would you add observability to a new service?"

Building an Observability Culture

Observability is as much about culture as tools. Teams that excel share certain characteristics:

Ownership Mentality

Engineers who build it also run it. This creates natural incentive to build observable systems.

Blameless Postmortems

Teams that learn from incidents without blame improve their observability over time.

Proactive Monitoring

Great teams don't wait for users to report problems. They detect and fix issues before customers notice.

Conclusion

Observability skills separate engineers who can write code from engineers who can keep systems running in production. As systems grow more complex and AI generates more code, these skills become only more valuable.

Yet traditional interviews completely ignore observability. Companies that learn to assess and hire for these skills will build teams that ship faster, break less, and resolve incidents before customers even notice.

In the age of AI-assisted development, being able to understand what's happening in production is a superpower. Make sure you're hiring engineers who have it.