Description

Job Specification: Splunk Observability Engineer

Synopsis

To design, implement, and optimize a full-stack observability strategy using the Splunk Observability Cloud (formerly SignalFx) and Splunk Enterprise/Cloud. You will ensure that engineering teams have 360-degree visibility into system health, moving the organization from reactive "firefighting" to proactive "pattern-based" incident prevention.

Key Responsibilities

- Data Orchestration: Architect the ingestion of the "Three Pillars" (Metrics, Logs, Traces) using OpenTelemetry (OTel) collectors.
- Aggregation Strategy: Develop logic to aggregate high-cardinality data to reduce "noise" while maintaining "signal" for troubleshooting.
- Analytical Modeling: Use SPL (Search Processing Language) and SignalFlow to perform pattern analysis, detecting anomalies before they trigger traditional threshold alerts.
- Visual Storytelling: Build executive and technical dashboards that correlate disparate data points (e.g., showing how a spike in 500-errors in Logs relates to a specific span in a Trace).

Required Hands on Technical Skills

1. Telemetry & Data Specialization

- Logs: Proficiency in "Logging-in-Context." You must be able to link logs directly to trace IDs so developers can jump from a failing trace to the specific line of code in the logs.
- Metrics: Expertise in SignalFlow (Splunk’s background streaming analytics language). You should know how to calculate percentiles ($P95, P99$), rates of change, and historical averages.
- Traces:



Deep understanding of Distributed Tracing. You must know how to instrument applications (Java, Python, Go) to capture spans and identify bottlenecks in microservices.

2. Pattern Analysis & Aggregation

- Anomaly Detection: Ability to configure Metric Finder and MDetector using standard deviations or "Mean Absolute Deviation" to find outliers.
- Data Scrubbing: Skills in using Splunk Ingest Actions or Edge Processors to filter, mask, or aggregate data at the edge to save on license costs and improve search speed.
- Pattern Discovery: Using Splunk’s machine learning commands (e.g., `findkeywords`, `cluster`) to group millions of log events into a few dozen "patterns" for faster root cause analysis.

3. Hands on - Dashboards & Visualization

- High-Cardinality Handling: Designing dashboards that don’t "break" when viewing thousands of containers.
- Contextual Drill-downs: Building "Glass Tables" (in ITSI) or Unified Dashboards that allow a user to click a metric and immediately see the associated logs.
- Frameworks: Familiarity with the Dashboard Studio and JSON-based dashboard definitions for version control (GitOps).

Preferred Qualifications & Certifications

- Splunk Cloud Certified Metrics User: Focuses on the metrics and alerting side.
- Splunk Core Certified Power User: Essential for mastering complex SPL for log analysis.
- OpenTelemetry Expert: Knowledge of the OTel Collector configuration (`receivers`, `processors`, `exporters`) is currently the most "in-demand" skill for this role.

📌 Splunk Observability Engineer (Hyderabad)
🏢 Leading
📍 Hyderabad