Fetcherr builds responsible AI that transforms market complexity into measurable profit growth.
At the core of the company is the Market Model - a proprietary AI-powered model delivering accurate, granular demand predictions with 96% forecast accuracy and real-time decision intelligence for commercial teams. Built on a glass-box architecture, it uses market data - not personal data - with full transparency into logic and outcomes. First deployed in global aviation, the technology is industry-agnostic and scales across volatile markets.
Fetcherr delivers a consistent average profit uplift of 7%, with corporate partners including Delta, Virgin Atlantic, WestJet, Viva, and Azul.
We're looking for a Senior Support Engineer to be part of the technical backbone of our support organization. You'll join a team of experienced support engineers, bringing together deep technical expertise and strong customer knowledge to resolve the most complex issues our enterprise customers face.
This role is the primary escalation point for our frontline support team — the person they turn to when an issue demands deeper investigation, broader product context, or a more senior technical hand. You'll be expected to lead from the front: diving into complex data pipeline failures, owning resolution end-to-end, creating runbooks, and domain expertise to continuously raise the bar as you always keep your knowledge up to date.
Beyond the technical work, you'll be a trusted face to our enterprise customers — attending customer meetings, communicating with clarity and confidence, and building the kind of credibility that makes customers feel in good hands even when things go wrong.
This is a role for someone who takes real ownership — of the quality of every escalation that moves upstream, and of the knowledge systems that make the entire support organization smarter over time.
Know how to take resolution within the SLA end-to-end — ensuring complex issues are resolved thoroughly, not just closed.
Be the technical escalation point from the frontline support team, picking up high-complexity issues and driving them to resolution with minimal unnecessary escalation to R&D.
Own the team's knowledge base and runbook ecosystem — making sure documentation is alive, accurate, and genuinely useful, not a graveyard of stale procedures.
Set and enforce R&D handoff quality standards — every escalation that reaches engineering should be well-investigated, clearly documented, and impossible to bounce back as "not enough info."
Develop deep product area expertise across Fetcherr's domains, distributing ownership across your team so that every product surface has a go-to engineer.
Represent the support function in customer meetings — you'll be the technical face of support for enterprise accounts, able to speak credibly about root causes, timelines, and remediation.
Collaborate closely with the Escalation Leader and R&D to close feedback loops, reduce recurring issues, and feed product insights back upstream.
Requirements
What You'll Bring
B.Sc. or higher in Computer Science, Engineering, or a related field.
5+ years of hands-on technical support or engineering experience in a B2B SaaS environment.
Strong technical depth — proficiency in Python and SQL is a must, with meaningful experience working with data pipelines and orchestration tools (Dagster or Airflow strongly preferred).
Proven experience owning and improving knowledge management systems — you know that a great runbook is a force multiplier.
Track record of reducing escalation noise — you've built or enforced criteria that kept R&D focused on what only R&D can solve.
Customer-facing confidence — you're comfortable in enterprise meetings, translating technical complexity into clear, trust-building communication.
Hands-on experience building agents and optimizing operational processes using agentic AI and vibe coding — you’ve designed and deployed AI-driven workflows that meaningfully reduced manual overhead and accelerated resolution cycles.
Strong written and verbal communication in English.
Nice to have
Experience in aviation, travel tech, or high-frequency dynamic pricing environments.
Familiarity with AI/ML product pipelines — understanding model outputs, feature data, and inference behavior at a level beyond surface troubleshooting.