Hiring Observability Engineers: The Complete Guide

What Observability Engineers Actually Do

Observability Engineering encompasses several core responsibilities that blend deep infrastructure knowledge with developer experience focus.

A Day in the Life

Instrumentation & Data Collection (30-40%)

• Instrumentation standards - Defining how services emit metrics, logs, and traces consistently
• OpenTelemetry adoption - Rolling out OTel SDKs, collectors, and semantic conventions
• Auto-instrumentation - Making instrumentation effortless for application teams
• Custom instrumentation - Building specialized instrumentation for business-critical flows
• Data quality - Ensuring telemetry is complete, accurate, and actionable

Pipeline & Infrastructure (25-35%)

• Telemetry pipelines - Building systems to collect, process, route, and store observability data at scale
• Data backends - Operating metrics stores (Prometheus, Thanos, Mimir), log aggregators (Elasticsearch, Loki), and trace backends (Jaeger, Tempo)
• Cost optimization - Managing observability costs through sampling, aggregation, and retention policies
• High availability - Ensuring observability systems remain available during the incidents they're meant to debug

Alerting & Incident Enablement (20-30%)

• Alert quality - Reducing alert fatigue through SLO-based alerting and actionable alerts
• Runbook integration - Connecting alerts to debugging workflows and automated remediation
• On-call tooling - Building systems that help responders diagnose issues faster
• Correlation engines - Connecting metrics, logs, and traces for unified debugging

Developer Experience (15-25%)

• Dashboards and visualization - Creating Grafana dashboards, exploratory tools, and service health views
• Self-service tooling - Enabling developers to create dashboards, alerts, and queries without observability expertise
• Documentation and training - Teaching teams how to use observability effectively
• Debug workflows - Building guided paths from alert to root cause

Observability vs. Monitoring: Understanding the Difference

This distinction matters for hiring. Many candidates use the terms interchangeably, but they represent different philosophies.

Monitoring: Predefined Questions

Traditional monitoring answers questions you knew to ask:

• Is the server up? (availability)
• Is disk usage above 80%? (threshold)
• Are we getting 5xx errors? (known failure mode)

Monitoring requires you to anticipate failures and create dashboards/alerts ahead of time. When novel problems occur, you're blind.

Observability: Arbitrary Questions

Observability enables questions you didn't know to ask:

• Why are requests from iPhone users in Germany slow?
• What changed between this deployment and last that caused latency to spike?
• Which customer's traffic triggered this database hotspot?

Observability requires high-cardinality data that lets you slice and dice by arbitrary dimensions (user_id, request_id, feature_flag, etc.).

Why It Matters for Hiring

Candidates who only talk about dashboards and thresholds have a monitoring mindset. Strong Observability Engineers think about:

• Cardinality - Can we query by any dimension without pre-aggregation?
• Correlation - Can we follow a request across services?
• Exploration - Can engineers debug without knowing what to look for?

The Three Pillars of Observability

Every Observability Engineer must understand the three pillars and their trade-offs.

Metrics

What they are: Numeric measurements aggregated over time (request count, latency percentiles, error rate)

Strengths:

• Cheap to store and query (pre-aggregated)
• Great for alerting and trends
• Low cardinality queries are fast

Weaknesses:

• High cardinality is expensive
• Can't trace individual requests
• Lose detail through aggregation

Key concepts to assess:

• Prometheus data model (labels, time series)
• Histogram vs. summary trade-offs
• Cardinality explosion risks

Logs

What they are: Timestamped records of discrete events (request completed, error occurred, user action)

Strengths:

• Rich context and detail
• Can search for specific events
• Good for debugging individual requests

Weaknesses:

• Expensive to store at scale
• Searching is slower than metrics
• Unstructured logs are hard to analyze

Key concepts to assess:

• Structured logging patterns
• Log levels and when to use each
• Sampling strategies at scale

Traces

What they are: Records of requests flowing through distributed systems, showing timing and relationships

Strengths:

• Show the full request path
• Identify where time is spent
• Enable service dependency mapping

Weaknesses:

• Complex to implement correctly
• High storage costs for full traces
• Require propagation context everywhere

Key concepts to assess:

• Distributed tracing concepts (spans, trace context)
• Sampling strategies (head-based, tail-based)
• OpenTelemetry trace model

Career Progression

When to Hire Dedicated Observability Engineers

Observability work happens in every engineering organization, but dedicated roles are context-dependent.

Strong Signal for Dedicated Role

• Scale - Processing billions of metrics/logs daily requires specialization
• Multi-team organizations - 50+ engineers need consistent observability standards
• Observability as platform - You're building internal observability tooling
• Vendor complexity - Managing Datadog/New Relic/custom stack requires expertise
• Cost management - Observability spend exceeds $100K/month

When SRE/Platform Covers It

• Smaller organizations - Under 30 engineers, SREs handle observability as part of reliability work
• Simple architectures - Monoliths or few microservices need less specialized tooling
• Vendor-managed solutions - Fully managed Datadog/New Relic reduces operational burden
• Mature practices - When observability is already well-established, maintenance is part-time

The Hybrid Reality

Most companies don't have dedicated "Observability Engineers"—the work is distributed across:

• SREs - Own alerting, incident tooling, and reliability metrics
• Platform Engineers - Own telemetry pipelines and developer tooling
• Application teams - Own service-specific instrumentation

When hiring, be clear: Are you building a dedicated observability team, or adding observability expertise to an existing SRE/Platform function?

Where to Find Observability Engineers

Backend Engineers at Observability Vendors

Engineers from Datadog, New Relic, Honeycomb, Grafana Labs, or Splunk have deep observability expertise. They've built the tools others use.

Why they work: Deep expertise, understand scale challenges
Watch out for: May have narrow tool expertise; compensation expectations may be high

SREs with Observability Focus

Site Reliability Engineers who've specialized in monitoring, alerting, and debugging tooling often make excellent Observability Engineers. They understand the production context.

Why they work: Production experience, understand on-call pain points
Watch out for: May focus on ops over developer experience

Platform Engineers Building Internal Tools

Engineers who've built internal observability platforms understand both the technical challenges and the developer experience requirements.

Why they work: Full-stack observability experience, user empathy
Watch out for: May lack depth in specific areas (tracing, metrics stores)

Open Source Contributors

Contributors to OpenTelemetry, Prometheus, Jaeger, Grafana, or similar projects demonstrate expertise publicly. These communities are excellent talent pools.

Why they work: Proven expertise, community engagement
Watch out for: May prefer open source work to company-specific challenges

Common Hiring Mistakes

1. Confusing Monitoring with Observability

Candidates who only talk about Nagios, uptime checks, and threshold alerts have a monitoring mindset. True Observability Engineers think about high-cardinality data, distributed tracing, and enabling exploration.

2. Over-Indexing on Tool Knowledge

Tools change rapidly. A candidate who knows Datadog deeply but lacks fundamentals will struggle when you migrate to Grafana. Focus on concepts: what makes good instrumentation? How do you design telemetry pipelines? What makes alerts actionable?

3. Ignoring Developer Experience

Observability is only valuable if developers use it. Candidates who focus solely on infrastructure without considering how application teams will instrument, query, and debug miss half the role.

4. Unclear Scope

Is this role about building telemetry pipelines, improving alert quality, or enabling self-service? Be specific. "Own observability" is too vague—define the actual problems you need solved.

5. Expecting Instant Results

Observability improvements take time. Migrating to OpenTelemetry, reducing alert noise by 50%, or building reliable trace correlation takes quarters, not weeks. Set realistic expectations.

Red Flags in Observability Candidates

• Only knows one tool - Can't discuss trade-offs or alternatives to their preferred stack
• No developer empathy - Builds tools for themselves, not for application teams
• Dashboard-centric thinking - Only talks about visualization, not data quality or instrumentation
• Can't explain the three pillars - Missing foundational knowledge
• No cost awareness - Doesn't understand observability economics at scale
• Alert-happy - Believes more alerts mean better observability
• Ignores correlation - Treats metrics, logs, and traces as separate problems

Interview Focus Areas

Observability Fundamentals

• Understanding of metrics, logs, and traces trade-offs
• OpenTelemetry knowledge and semantic conventions
• Instrumentation patterns for different languages/frameworks

System Design for Observability

• Telemetry pipeline architecture at scale
• Sampling strategies and their trade-offs
• Cost management and data retention decisions

Alerting Philosophy

• SLO-based alerting vs. threshold alerting
• Reducing alert fatigue and improving signal-to-noise
• On-call experience and incident debugging

Developer Experience

• Self-service tooling approaches
• Documentation and training strategies
• Balancing flexibility with consistency