Observability Engineer

Cardinality Management

Por qué: 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

Por qué: 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

Por qué: 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

Por qué: 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

Por qué: 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

Por qué: 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

Por qué: 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

Por qué: 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

Por qué: 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

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