Data Engineer - Support

Key Responsibilities:

• Issue Troubleshooting & Customer Support:
• Investigate and debug complex customer-reported issues related to data pipelines, integrations, data models, and Cognite Data Fusion functionality.
• Collaborate with customer teams to understand production incidents, reproduce issues, and implement reliable fixes or workarounds.
• Analyze data failures, system errors, and unexpected behaviors across API integrations, custom code, and third-party data sources.
• Maintain and improve the reliability and performance of data pipelines by identifying bottlenecks, configuration mismatches, and usage anomalies.
• Work with engineering and product teams to escalate platform issues, suggest improvements, and contribute to long-term solutions.
• Advanced and Enterprise-Level Support:
• Serve as the technical point of contact for complex and high-priority support cases.
• Perform in-depth troubleshooting, diagnostics, and custom code reviews for third-party integrations.
• Conduct root cause analyses, case reviews, and provide updates aligned with customer SLAs.
• Participate in on-call rotations to ensure 24/7 coverage and rapid response to critical issues.
• Implement and manage proactive monitoring and alerting systems to detect and address issues early.
• Customer Enablement & Knowledge Sharing:
• Create and maintain documentation, how-to guides, and knowledge base articles to improve resolution times and reduce repeat incidents.
• Collaborate with product and engineering teams to provide customer feedback and help prioritize enhancements.

Required Skills & Qualifications:

• Education: Bachelor's or Master's degree in Computer Science, Engineering, or related field.
• Experience: 3–5 years in a data-intensive, customer-facing role with hands-on technical support experience in SaaS or data platform environments (Tier 2/3 or engineering support preferred).
• Programming: Strong proficiency in Python and SQL, with experience working on REST APIs.
• Cloud Platforms: Familiarity with Azure, GCP, Kubernetes, and CI/CD tools.
• Data Pipelines: Experience maintaining production-grade data pipelines and workflows in live environments.
• Domain Knowledge: Understanding of industrial data systems or domains like Oil & Gas, Power, or Manufacturing is a plus.
• Tools: Experience with Grafana, Power BI, or GraphQL is advantageous.
• Soft Skills: Strong problem-solving, troubleshooting, and communication skills with a customer-centric approach.