The Mid Observability Engineer focuses on optimizing, scaling, and improving observability platforms operating in high-volume, distributed environments across multiple client projects.
This role goes beyond basic monitoring implementation and emphasizes platform-level improvements, data quality, performance tuning, and cost efficiency. You will work closely with platform, AI, and automation teams to ensure observability data is reliable, well-structured, and suitable for advanced analytics and intelligent systems.
- Optimize and scale observability platforms in distributed, high-traffic environments
- Tune alerting rules and thresholds to reduce noise and improve signal quality
- Design and manage data retention, aggregation, and downsampling strategies
- Improve performance and reliability of metrics, logs, and tracing pipelines
- Configure and optimize OpenTelemetry Collectors and data processing pipelines
- Support advanced analytics and correlation use cases
- Collaborate with AI and automation teams to feed high-quality observability data into intelligent workflows
- Identify bottlenecks, inefficiencies, and cost drivers in observability systems
- Contribute to platform-level design discussions and technical improvements
- Grafana (advanced dashboards and analytics)
- OpenTelemetry Collectors
- Message queues and streaming systems
- Cloud storage services
- Python-based services and tooling
- Distributed data pipelines
- 2–5 years of experience in observability, monitoring, or platform engineering
- Strong hands-on experience with Grafana and observability tooling
- Experience operating observability systems in distributed environments
- Practical experience with OpenTelemetry Collectors or similar pipeline components
- Understanding of alert fatigue, signal-to-noise optimization, and alert design
- Experience managing data retention, aggregation, or downsampling strategies
- Solid understanding of Linux systems and containerized environments
- Basic to intermediate scripting experience (Python or Bash)
- Experience with message queues or streaming platforms
- Experience supporting AI-driven or automation systems
- Familiarity with cloud-native storage and cost optimization
- Exposure to SRE practices, SLIs, SLOs, and error budgets
- Experience working with enterprise-scale platforms
- Independently owns observability improvements within a platform or domain
- Proactively identifies and resolves scalability, performance, and cost issues
- Influences observability standards and best practices
- Communicates effectively with platform, AI, and automation stakeholders
- Bishkek, Kyrgyzstan
- Tashkent, Uzbekistan
Please send your CV to:
Subject line: Mid Observability Engineer (Level 2) Application