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Senior Support Engineer
On-siteFull-timeCustomer SupportEnterprise Software
Join an experienced support organization as the senior escalation point for complex issues affecting enterprise customers of an AI-powered SaaS platform. Own investigations through resolution, improve support systems, and represent the technical support function in customer discussions.
Responsibilities
- Resolve complex customer issues end-to-end within service-level commitments.
- Act as the technical escalation point for frontline support and minimize unnecessary handoffs to R&D.
- Investigate complex data pipeline failures and drive thorough resolution.
- Own and maintain the support knowledge base and runbook ecosystem.
- Set and enforce standards for investigation quality and documentation before R&D escalation.
- Develop product-area expertise and distribute technical ownership across the support team.
- Represent support in enterprise customer meetings, explaining root causes, timelines, and remediation.
- Collaborate with escalation leadership and R&D to reduce recurring issues and share product insights.
- Keep technical and product knowledge current.
Requirements
- Bachelor’s degree or higher in Computer Science, Engineering, or a related technical field.
- At least 5 years of hands-on technical support or engineering experience in a B2B SaaS environment.
- Proficiency in Python and SQL.
- Meaningful experience with data pipelines and orchestration tools; Dagster or Airflow experience is preferred.
- Experience building and improving knowledge management systems and runbooks.
- Experience defining or enforcing escalation criteria that improve the quality of R&D handoffs.
- Confidence communicating with enterprise customers and translating technical issues into clear explanations.
- Hands-on experience building agents and optimizing operational processes with agentic AI and vibe coding.
- Strong written and verbal English communication skills.
Nice to have
- Experience in aviation, travel technology, or high-frequency dynamic pricing environments.
- Familiarity with AI/ML product pipelines, including model outputs, feature data, and inference behavior.