Observability Engineer (KUBERNETES & CONTAINERS, GITOPS & AUTOMATION, QUERY LANGUAGES, CLOUD & INFRASTRUCTURE)

Apptoza Inc.; Toronto; Hybrid; CAD 120,000 - 160,000; Full time

Job summary

Apptoza Inc. is seeking an experienced Observability Engineer to own the enterprise observability stack across 50+ Kubernetes clusters.

You will design, deploy, and maintain metrics, logs, tracing, and alerting with Prometheus, Grafana, Loki, and related tools, while applying GitOps and IaC to enable scalable, self-healing infrastructure. You will drive intelligent monitoring using AI/ML capabilities and collaborate with SRE, DevOps, and platform teams to optimize performance, reliability, and

Qualifications

• 4-6 years of experience in observability, monitoring, SRE, or platform engineering roles.
• 3+ years hands-on production experience with Prometheus and Grafana.
• 2+ years working with Kubernetes and containerized environments.
• Strong expertise with PromQL for metrics querying and alerting.
• Experience deploying and managing observability stacks at scale (1000+ nodes).
• Proven track record of reducing MTTR through effective observability.

Responsibilities

• Design, deploy, and maintain enterprise-scale observability infrastructure across clusters and environments.
• Manage observability deployments using GitOps principles and infrastructure as code
• Create dashboards and alerts, and enable multi-datasource visualization in Grafana
• Configure logging and tracing pipelines to integrate with metrics
• Collaborate with SRE, DevOps, and platform teams to optimize reliability and performance

Skills

Observability

PromQL

Kubernetes

SRE practices

AI/ML familiarity

Tools

Prometheus

Grafana

Thanos

Loki

OpenTelemetry

Jaeger

Tempo

Elasticsearch

Job description

Observability Engineer (KUBERNETES & CONTAINERS, GITOPS & AUTOMATION, QUERY LANGUAGES, CLOUD & INFRASTRUCTURE)

Toronto, On

Hybrid - 4 days

ABOUT THE ROLE

We are seeking an experienced Observability Engineer to join our Enterprise Kubernetes Platform team at a leading financial services organization. You'll own the complete observability stack across 50+ production Kubernetes clusters, providing metrics, logging, tracing, and alerting capabilities that ensure exceptional reliability and performance for mission-critical applications.

This role combines deep technical expertise in modern observability tools with emerging AI/ML capabilities to build intelligent monitoring solutions, predictive alerting, and self-healing infrastructure.

WHAT YOU'LL DO

OBSERVABILITY STACK OWNERSHIP

• Design, deploy, and maintain enterprise-scale observability infrastructure including Prometheus, Grafana, Thanos, Loki, and modern collection agents
• Manage observability deployments using GitOps principles and infrastructure as code
• Implement long-term metrics storage solutions with cloud object storage
• Maintain and upgrade observability components across development, QA, UAT, production, and DR environments
• Configure distributed observability architecture spanning multiple data centers and cloud providers

METRICS & MONITORING

• Design and implement Prometheus monitoring strategies for Kubernetes infrastructure and containerized applications
• Create ServiceMonitors, PodMonitors for automated metrics collection
• Develop rules for intelligent alerting with minimal false positives
• Configure multi-cluster metrics federation and aggregation
• Optimize metrics cardinality, storage e_iciency, and query performance
• Implement recording rules for pre-aggregated metrics and SLI calculations

DASHBOARDS & VISUALIZATION

• Build comprehensive Grafana dashboards for infrastructure health, application performance, and business metrics
• Create reusable dashboard templates and libraries for development teams
• Implement dashboard-as-code
• Configure multiple datasources
• Design executive dashboards with SLO/SLI tracking and business KPIs
• Implement role-based access control and multi-tenancy in Grafana

LOGGING INFRASTRUCTURE

• Deploy and manage centralized logging solutions (Loki, ELK, Splunk, or similar)
• Configure log collection agents (Promtail, Fluentd, FluentBit, Vector, etc.)
• Design log retention policies balancing cost, compliance, and operational needs
• Create LogQL/Lucene queries and log-based alerts
• Implement log correlation with metrics and traces for unified troubleshooting
• Build log aggregation pipelines with parsing, filtering, and enrichment

ALERTING & INCIDENT MANAGEMENT

• Configure intelligent alerting with Alertmanager or equivalent platforms
• Design alert rules with appropriate severity levels, thresholds, and SLOs
• Implement alert routing to Slack, PagerDuty, ServiceNow, email, and webhooks
• Create automated runbooks and remediation workflows
• Develop alert inhibition, silencing, and grouping strategies
• Tune alerting to achieve signal-to-noise ratio improvements
• Integrate with incident management and on-call rotation systems

DISTRIBUTED TRACING

• Implement distributed tracing solutions using OpenTelemetry, Jaeger, or Tempo
• Instrument applications for trace collection and correlation
• Configure trace sampling strategies for cost and performance optimization
• Build trace-based dashboards for latency analysis and dependency mapping
• Integrate tracing with metrics and logs for comprehensive observability

AI/ML FOR OBSERVABILITY

• Implement AI-powered anomaly detection for metrics and logs
• Build predictive alerting using machine learning models to forecast issues before they occur
• Develop intelligent alert correlation and root cause analysis systems
• Integrate LLM-based tools for log analysis and troubleshooting assistance
• Implement AIOps capabilities for automated incident triage and resolution
• Use AI to optimize alert thresholds and reduce false positives
• Build natural language query interfaces for observability data
• Implement Model Context Protocol (MCP) for AI agent integration with observability platforms

AUTOMATION & PLATFORM INTEGRATION

• Automate observability deployment using GitOps workflows (FluxCD, ArgoCD)
• Integrate with CI/CD pipelines for automated testing and validation
• Build self-service portals for teams to create dashboards and alerts
• Develop APIs and CLIs for observability automation
• Integrate with secrets management solutions (Vault, AWS Secrets Manager)
• Configure LDAP/AD/SSO authentication for observability platforms
• Automate compliance reporting and audit logging

ENABLEMENT & COLLABORATION

• Onboard application teams to observability platforms
• Provide guidance on instrumentation best practices
• Create documentation, training materials, and self-service guides
• Conduct workshops
• Support development teams during incidents with observability insights
• Collaborate with SRE, DevOps, and platform engineering teams

PERFORMANCE & COST OPTIMIZATION

• Monitor and optimize observability stack resource consumption
• Implement autoscaling for stateless observability components
• Tune data retention, compaction, and downsampling strategies
• Conduct capacity planning for metrics and log storage growth
• Optimize query performance and dashboard response times
• Implement cost allocation and chargeback for multi-tenant environments

REQUIRED QUALIFICATIONS

EXPERIENCE

• 4-6 years of experience in observability, monitoring, SRE, or platform engineering roles
• 3+ years hands-on production experience with Prometheus and Grafana
• 2+ years working with Kubernetes and containerized environments
• Strong expertise with PromQL for metrics querying and alerting
• Experience deploying and managing observability stacks at scale (1000+ nodes)
• Proven track record of reducing MTTR through e_ective observability

CORE TECHNICAL SKILLS

OBSERVABILITY PLATFORMS:

• Prometheus (including Prometheus Operator)
• Grafana (dashboards, alerting, plugins)
• Thanos, Cortex, or Mimir for long-term storage
• Alertmanager or equivalent alerting platforms

LOGGING:

• Loki, Elasticsearch/ELK Stack, Splunk, or CloudWatch Logs
• Log collection agents (Promtail, Fluentd, FluentBit, Vector)
• LogQL, Lucene, or equivalent query languages

TRACING:

• OpenTelemetry (OTEL Collector, instrumentation)
• Jaeger, Zipkin, Tempo, or AWS X-Ray
• Trace sampling and correlation strategies

KUBERNETES & CONTAINERS:

• Kubernetes architecture and operations (1.24+)
• Custom Resource Definitions (CRDs) and Operators
• ServiceMonitor, PodMonitor, PrometheusRule resources
• Kubernetes metrics (kube-state-metrics, node-exporter, cAdvisor)

GITOPS & AUTOMATION:

• Basic SQL for data analysis

CLOUD & INFRASTRUCTURE:

• AWS, Azure, or GCP cloud platforms
• Multi-cloud and hybrid architectures
• vSphere or on-premises virtualization (nice to have)

AI/ML & EMERGING TECHNOLOGIES

• Experience with AI/ML frameworks for observability (Prophet, TensorFlow, PyTorch)
• Anomaly detection algorithms and time-series forecasting
• LLM integration for log analysis and troubleshooting (GPT, Claude, etc.)
• Model Context Protocol (MCP) for AI agent integration
• AIOps platforms (Moogsoft, BigPanda, Datadog Watchdog, etc.)
• Natural language processing for log parsing and analysis
• Familiarity with vector databases for semantic search (Pinecone, Weaviate)
• Experience with AI-powered root cause analysis tools
• Knowledge of prompt engineering for observability use cases

PREFERRED QUALIFICATIONS

• Kubernetes certifications (CKA, CKAD, or CKS)
• Grafana certification or equivalent training
• Knowledge of service mesh observability (
• Experience with SRE practices, SLIs, SLOs, and error budgets
• Background in financial services or regulated industries
• Familiarity with compliance requirements (SOX, PCI-DSS, etc.)
• Contributions to open-source observability projects
• Experience with APM tools