Observability Engineer

Observability Engineer

Company Overview:

Crest Data is the global leading provider of Data Analytics, Security, DevOps, Cloud Solutions, Software integrations, Analytics, and security-based technological services. With a clientele that includes several Fortune 500 corporations and some of the innovative Silicon Valley Startups.

Company URL: http://www.crestdata.ai

Job Location - Ahmedabad

Experience : 4+ Years

Role Summary:

We are hiring an Observability Engineer/Consultant to design, implement, and optimize observability solutions across complex customer or internal environments. You will own the technical delivery of observability use cases — from instrumentation and data pipeline configuration to dashboards, alerting, and SLO frameworks. This role sits at the intersection of platform engineering and consulting, requiring both hands-on tool depth and the ability to communicate technical decisions clearly to engineering and operations stakeholders.

Key Responsibilities

• Deploy and configure observability platforms (Dynatrace, Datadog, Elastic, Splunk, New Relic, or equivalent) across on-premises, cloud, and hybrid environments.
• Instrument applications and infrastructure to collect metrics, logs, and distributed traces aligned to business and operational requirements.
• Design and build dashboards, alerting rules, and SLI/SLO frameworks that surface actionable signal rather than noise.
• Define and enforce standards for log structuring, trace propagation, and metric naming conventions across engineering teams.
• Integrate observability tooling into CI/CD pipelines, cloud-native infrastructure (Kubernetes, Docker), and data pipeline components (e.g., OpenTelemetry Collectors, Cribl, Logstash).
• Conduct observability assessments for customer environments, identify coverage gaps, and deliver implementation roadmaps with clear prioritization.
• Produce technical documentation including architecture diagrams, runbooks, onboarding guides, and platform-specific configuration standards.

Required Qualifications:

• 4+ years of hands-on experience implementing or consulting on observability solutions in production environments.
• Demonstrable proficiency with at least one enterprise observability platform: Dynatrace, Datadog, New Relic, Elastic Stack (ELK/EFK), or Splunk — including agent deployment, data ingestion configuration, and use case development.
• Working knowledge of all three observability pillars: Metrics (collection, aggregation, PromQL or equivalent), Logs (structured logging, pipeline configuration, query languages), and Traces (distributed tracing, context propagation, APM instrumentation).
• Experience with OpenTelemetry (SDK instrumentation, Collector configuration, OTLP pipelines) or equivalent vendor-native tracing agents.
• Familiarity with containerized and cloud-native environments: Kubernetes, Docker, and at least one major cloud provider (AWS, Azure,
• or GCP).Ability to read and write scripts or queries in at least one of: Python, Bash, SPL, DQL, KQL, LogQL, PromQL, or equivalent.
• Strong technical communication skills — able to produce clear documentation and explain architectural decisions to both engineering teams and non-technical stakeholders.

Nice-to-Have Qualifications:

• Platform certifications: Dynatrace Associate or Professional, Datadog Fundamentals, Splunk Core Certified Power User or Splunk Admin, Elastic Certified Engineer, or New Relic Observability Practitioner.
• Experience with log and telemetry pipeline tools: Cribl Stream, Fluentd, Vector, or Kafka-based architectures.
• Exposure to SIEM platforms (Splunk ES, Palo Alto Cortex XSIAM, Microsoft Sentinel) and understanding of where observability and security telemetry overlap.
• Familiarity with Infrastructure as Code tools (Terraform, Ansible) for managing observability configurations at scale.
• Experience working in a managed service, MSSP, or customer-facing consulting capacity supporting multi-tenant or multi-environment deployments.

What We Offer:

• A technically grounded team where engineering decisions are evidence-based and observability is treated as a first-class engineering discipline, not an after thought.
• Exposure to diverse technology stacks and customer environments, with real ownership of implementation outcomes.
• Structured career development with support for platform certifications and continued technical specialization.
• Competitive compensation, flexible working arrangements, and a transparent performance review process.

Skills Required
Docker, Splunk, Kubernetes, Dynatrace, Bash, Gcp, Dql, New Relic, Datadog, SPL, Azure, Aws, Python