- Support Systems Engineering Lifecycle:
- Engage in requirements gathering, design, testing, implementation, operations, and documentation for large hybrid Splunk and Cribl deployments.
- Log Data Pipelines:
- Implement log data pipelines through automation in Python to ingest logs into platforms like Splunk and Open Search.
- Platform Automation:
- Automate platform management processes using Ansible or other scripting tools/languages.
- Incident Troubleshooting:
- Troubleshoot incidents impacting the log data platforms and collaborate with users of the platform.
- Documentation and Training:
- Develop training and documentation materials to support the log data platform.
- Platform Upgrades:
- Support log data platform upgrades, coordinating testing of upgrades with users.
- Data Processing:
- Gather and process raw data from multiple disparate sources, using scripts, APIs, and SQL queries for analysis.
- Log Data Engineering:
- Build log data pipelines to assist in the development and testing of log data engineering solutions.
- User Support:
- Provide support for technical users and conduct requirements analysis.
Experience and Skills:
General:
- Strong troubleshooting and diagnostic skills for complex issues.
- Experience in supporting technical users and conducting requirements analysis.
- Ability to work independently with minimal oversight.
- Familiarity with IT Service Management, Incident & Problem Management.
- Proficient in identifying performance bottlenecks, diagnosing anomalous system behavior, and resolving root cause issues.
- Effective cross-team collaboration to influence design, operations, and deployment of highly available software.
- Knowledge of best practices related to security, performance, and disaster recovery.
Required Technical Expertise:
- 3-5 years of experience managing and configuring Splunk Enterprise and/or Splunk Cloud.
- Experience with Linux and Windows agents (Splunk, Fluentbit/Fluentd) for log data engineering.
- Proficiency in designing, developing, and deploying cloud-based solutions using AWS.
- Experience onboarding new data, configuring, creating dashboards, and extracting information via Splunk and Cribl.
- Development of systems for data extraction, ingestion, and processing large volumes of data.
- Proficiency in scripting and automation (bash, python, other programming languages).
- Familiarity with Splunk REST APIs.
- Knowledge of cloud platforms (preferably AWS) and container/orchestration technologies.
- Experience with data pipeline orchestration platforms.
Preferred Technical Experience:
- Splunk Certification (Admin or Architect).
- Experience with Ansible Tower automations.
- Experience using GitLab.
- Experience with large platform migration efforts.
- Experience with AWS OpenSearch.
- Experience with Cribl.
- Familiarity with data streaming technologies such as Kafka, Kinesis, and Spark Streaming.