Job Description:

Design and deploy scalable, standardized, and maintainable data pipelines that enable efficient logging, error handling, and real-time data enrichment. The role requires strong ownership of both implementation and performance.

Responsibilities

· Optimize Splunk queries and search performance using best practices

· Build and manage data ingestion pipelines from sources like Kafka, APIs, and log streams

· Standardize error structures (error codes, severity levels, categories)

· Create mappings between identifiers such as session ID, user ID, and service/module components

· Implement real-time data enrichment processes using APIs, databases, or lookups

· Set up alerting configurations with thresholds, modules, and logic-based routing

· Collaborate with developers, DevOps, and monitoring teams to unify logging conventions

· Document flows and ensure traceability across environments

Job Requirements

· Minimum 3 years of hands-on experience in Splunk – Mandatory

· Proficient in SPL, data parsing, dashboards, macros, and performance tuning – Mandatory

· Experience working with event-driven systems (e.g., Kafka, REST APIs) – Mandatory

· Deep understanding of structured/semi-structured data (JSON, XML, logs) – Mandatory

· Strong scripting ability with Python or Bash

· Familiar with CI/CD processes using tools like Git and Jenkins

· Experience with data modeling, enrichment logic, and system integration

· Advantage: familiarity with log schema standards (e.g., ECS, CIM)

· Ability to work independently and deliver production-ready, scalable solutions – Mandatory