Booking Infrastructure Observability
End-to-end monitoring for the booking flow across 1,000+ microservices. APM tracing, custom business metrics, and intelligent alerting that reduced incident response time by 40%.
Trading Platform Monitoring
Real-time observability for cryptocurrency trading where latency is critical. SLO tracking, security monitoring integration, and capacity planning for volatile traffic patterns.
Connected Fitness Telemetry
Monitoring for live streaming classes and millions of connected devices. Device health dashboards, streaming quality metrics, and predictive alerting for class capacity.
Payment API Observability
Comprehensive monitoring for payment processing infrastructure. End-to-end trace analysis, error budget tracking, and performance regression detection in CI/CD.
What Datadog Engineers Actually Build
Before writing your job description, understand what observability work looks like at different companies. Here are real examples from industry leaders:
Travel & Hospitality
Airbnb uses Datadog to monitor their entire booking infrastructure. Their observability engineers handle:
- APM tracing across 1,000+ microservices to identify slow checkout flows
- Custom dashboards for business metrics (bookings per minute, search latency)
- Intelligent alerts that wake on-call engineers only for real customer-impacting issues
- Log correlation to debug payment failures across multiple services
Fintech & Crypto
Coinbase relies on Datadog for monitoring their trading platform where milliseconds matter. Their team builds:
- Real-time dashboards showing transaction throughput and latency percentiles
- SLO monitors that track 99.9% availability commitments
- Security monitoring integration to detect unusual trading patterns
- Custom metrics for blockchain-specific operations
Stripe uses Datadog for their payment infrastructure:
- End-to-end trace analysis from merchant API call to bank settlement
- Error budget tracking for their public API reliability
- Performance regression detection in CI/CD pipelines
E-Commerce & Retail
Peloton monitors their connected fitness platform with Datadog:
- Live class streaming metrics (buffering rates, connection drops)
- Device telemetry from millions of bikes and treadmills
- Capacity planning dashboards for peak class times
Understanding the Observability Stack
The Three Pillars: Metrics, Logs, and Traces
Strong Datadog engineers understand how these work together:
| Pillar | What It Shows | Example Question It Answers |
|---|---|---|
| Metrics | Aggregated measurements over time | "Is our error rate above 1%?" |
| Logs | Individual event records | "What error message did user X see?" |
| Traces | Request flow across services | "Which service caused the 500ms latency spike?" |
Junior engineers treat these as separate tools. Senior engineers correlate them: "The spike in errors (metric) at 3:14 PM correlates with these stack traces (logs) from the payment service, and the APM trace shows the database query took 4 seconds (trace)."
APM vs. Infrastructure Monitoring
A common mistake in hiring: conflating APM skills with infrastructure monitoring. They're different disciplines:
Infrastructure Monitoring (servers, containers, cloud resources):
- CPU, memory, disk, network utilization
- Container orchestration metrics (Kubernetes pod health)
- Cloud provider metrics (AWS CloudWatch integration)
Application Performance Monitoring (code-level visibility):
- Request latency and throughput per endpoint
- Distributed traces across microservices
- Database query performance and N+1 detection
The best observability engineers excel at both, but early-career candidates often specialize. Know which you need.
Skills by Experience Level
Junior Datadog Engineer
- Creates dashboards from existing metrics
- Sets up basic integrations (AWS, Docker, common databases)
- Configures threshold-based alerts
- Understands metric types (gauges, counters, histograms)
- Uses Datadog's UI for basic troubleshooting
Mid-Level Datadog Engineer
- Designs monitoring strategies for new services
- Implements distributed tracing with proper context propagation
- Creates effective alerting with minimal noise
- Manages costs through retention policies and sampling
- Builds custom metrics and instrumentation
- Understands SLIs and SLOs conceptually
Senior Datadog Engineer
- Architects observability platforms for 100+ services
- Establishes SLI/SLO frameworks that drive engineering decisions
- Optimizes for cost while maintaining visibility
- Leads incident response and postmortem processes
- Implements Datadog-as-Code with Terraform
- Mentors teams on observability best practices
- Evaluates build vs. buy decisions (Datadog vs. open-source alternatives)
Datadog vs. Open Source Stack
This is one of the most common questions hiring managers ask. Here's a balanced comparison:
| Aspect | Datadog | Prometheus + Grafana + ELK |
|---|---|---|
| Setup time | Hours to days | Days to weeks |
| Operational burden | Managed by Datadog | Your team manages it |
| Cost model | Per-host/per-metric pricing | Infrastructure + engineering time |
| Cost at scale | Can be very expensive | More predictable |
| Correlation | Built-in across pillars | Manual integration |
| Customization | Platform-bound | Fully customizable |
| Vendor lock-in | Yes (export is possible) | Open standards |
When to hire Datadog specialists:
- You're already committed to Datadog (significant investment)
- You need unified observability quickly
- You have budget but limited SRE headcount
When to consider open-source backgrounds:
- You're cost-sensitive at scale (1000+ hosts)
- You need deep customization
- You want to avoid vendor lock-in
Many companies use both: Datadog for APM and alerting, Prometheus for granular infrastructure metrics.
Datadog vs. New Relic vs. Splunk
Beyond open source, recruiters often ask how Datadog compares to commercial alternatives:
| Aspect | Datadog | New Relic | Splunk |
|---|---|---|---|
| Primary strength | Unified observability | APM & full-stack | Log analytics & SIEM |
| Pricing model | Per-host, per-metric | Per-user & per-GB | Per-GB ingestion |
| Cost predictability | Variable at scale | User-based is predictable | Can be expensive |
| Kubernetes native | Excellent | Good | Requires setup |
| Log management | Strong | Good | Industry-leading |
| Security features | Growing (Cloud SIEM) | Limited | Market leader |
Candidate transferability: Engineers moving between these platforms adapt quickly—the concepts (metrics, traces, alerts) transfer directly. Don't over-filter for one platform.
Recruiter's Cheat Sheet: Spotting Great Candidates
Conversation Starters That Reveal Skill Level
| Question | Junior Answer | Senior Answer |
|---|---|---|
| "How do you decide what to alert on?" | "We alert on everything important" | "We alert on symptoms that impact users, not causes. CPU at 90% isn't an alert—but response time degradation is." |
| "Tell me about reducing alert fatigue" | Generic or vague | "We reduced pager volume 60% by converting threshold alerts to anomaly detection and implementing alert grouping" |
| "What's your approach to dashboards?" | "I put all the metrics on one dashboard" | "Different dashboards for different audiences: executive (business KPIs), on-call (debugging), capacity planning" |
Resume Signals That Matter
✅ Look for:
- Specific incidents they helped resolve ("Reduced MTTR from 45 min to 15 min")
- SLO/SLI experience ("Established 99.9% availability target")
- Cost optimization ("Reduced Datadog spend 30% while maintaining coverage")
- Terraform or infrastructure-as-code for monitoring
- On-call experience and incident response
🚫 Be skeptical of:
- Only mentions "created dashboards" with no context
- Lists every monitoring tool (Datadog AND New Relic AND Splunk AND Prometheus)
- No mention of incident response or on-call
- "Expert in Datadog" but no specific implementations
Portfolio Red Flags
- Can't explain their alerting philosophy
- Never been on-call or involved in incident response
- Dashboards are just metrics dumps with no clear purpose
- No understanding of costs or retention policies
Common Hiring Mistakes
1. Requiring Datadog Certification
Certifications show study habits, not production experience. Someone who's been on-call for a high-traffic service and used Datadog during incidents is more valuable than someone who passed a multiple-choice exam.
Better approach: Ask about real incidents they've debugged using observability tools.
2. Ignoring Cost Awareness
Datadog billing can surprise teams. At scale, costs can reach $500K+ annually. A senior observability engineer should understand:
- Per-host vs. per-metric pricing models
- Retention policies and their cost implications
- When to sample vs. collect everything
3. Conflating Datadog with SRE
Datadog expertise is one skill within SRE. Don't hire "a Datadog engineer" when you need someone who can also:
- Design reliability architectures
- Implement chaos engineering
- Build deployment pipelines
- Manage incidents end-to-end
4. Over-Specifying the Platform
Great observability engineers learn tools quickly. If someone has deep Prometheus + Grafana experience, they'll learn Datadog in weeks. Focus on monitoring philosophy over platform-specific syntax.
Datadog Product Landscape
Modern Datadog extends far beyond basic monitoring. Understanding what's available helps you scope roles:
Core Products
- Infrastructure Monitoring: Host metrics, cloud integrations, containers
- APM & Distributed Tracing: Code-level performance visibility
- Log Management: Centralized logging with powerful search
- Synthetics: Proactive API and browser testing
- RUM: Real user experience monitoring
Advanced Capabilities
- Security Monitoring: Cloud SIEM and threat detection
- CI Visibility: Pipeline performance and test analytics
- Database Monitoring: Query-level insights without agents
- Network Performance Monitoring: Cross-cloud network visibility
- Profiling: Continuous code profiling in production
Most roles focus on the core products. Security Monitoring and CI Visibility are emerging specializations commanding premium salaries.