Data Pipeline Engineer

Overview

Data Pipeline Engineer

Key Responsibilities

• Solution Design & Implementation: Design, implement, and maintain solutions.
• Data Onboarding: Onboard complex data sources.
• Client Engagement: Manage client relationships and work directly with customers to understand their data and technology challenges.
• Consultation & Support: Provide pre-sales support and assist with customer training and adoption.
• Best Practices: Stay up to date on Cribl best practices to ensure optimal solution delivery.

Essential

• Proven experience in data logging/pipeline tools such as Splunk, Cribl or Elastic.
• Proven experience working with clients to manage demands, expectations, and business requirements.
• Proficiency in data onboarding activities, including developing custom parsing functions and transformations.
• Experience in writing complex queries, parsers and handling data streams
• Ability to solve complex problems with hands-on engineering tasks.
• 2+ years of experience with relevant data tool (i.e., Splunk, Elastic, Cribl).

Desirable

• Experience configuring and maintaining large data pipelining environments.
• Proficiency in scripting languages (e.g., Python, Bash) and writing regular expressions.
• Familiarity with containerized solutions (Kubernetes, Docker, OpenShift).
• Experience with cloud computing platforms (AWS, Azure, or GCP).
• Previous experience using PMO and ITSM tooling (ServiceNow, JIRA, Confluence).
• Formal certifications (preferred) in relevant data logging/pipeline tools such as Cribl, Splunk or Elastic.
• About you:
• Passionate about technology and technical development.
• Proactive approach to problem-solving with excellent analytical skills.
• Self-sufficient with strong written and verbal communication skills.
• Ability to engage with non-technical staff and collaborate effectively with peers.
• Strong team-playing mentality and ability to work to tight deadlines.