Observability Engineer

Cardinality Management

Why: You detect a label explosion, relabel at ingestion and downsample long-term data, so one team adding a user id label does not take the metrics backend down.

Log Retention & Cost Optimization

Why: You set tiered retention and sampling per log class and legal need, so the logging bill stops growing faster than traffic while audit data is kept.

Service Dependency Mapping

Why: You generate the dependency graph from trace data and estimate blast radius, so before a risky migration you know which services call the component being changed.

Sampling Strategies

Why: You choose head or tail sampling in the Collector so errors and slow traces are always kept, while routine traffic is cut to fit the storage budget.

SLO-Based Alerting

Why: You standardize SLO and burn rate alerting across teams, so release and freeze decisions are made from the same error budget view everywhere.

Runbook Automation

Why: You encode a recurring incident fix as an automated runbook triggered by an alert, so a known failure heals itself and time-to-remediation can be measured.

Observability Pipeline Design

Why: You design Collector pipelines that enrich, route and fan out telemetry reliably, so switching or adding a backend does not require changing every application.

eBPF & Kernel Observability

Why: You use eBPF tools such as Pixie or bpftrace to see network and syscall behavior without code changes, so you can debug a legacy service nobody can instrument.

Observability Culture & Adoption

Why: You set golden signals and onboarding standards and measure adoption, so a team that ships uninstrumented code is noticed and helped before its first incident.

Cost Management for Observability

Why: You forecast telemetry volume and build chargeback by team, so a surprise ingestion spike is traced to its owner and discussed with numbers.