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Fetcherr builds responsible AI that transforms market complexity into measurable profit growth. The company's core technology is the Market Model—a proprietary AI-powered system delivering 96% forecast accuracy and real-time decision intelligence for commercial teams. Built on a glass-box architecture using market data with full transparency, it has been deployed in global aviation with corporate partners including Delta, Virgin Atlantic, WestJet, Viva, and Azul, delivering an average profit uplift of 7%.
As VP Engineering, you will serve as a senior technical and organizational leader responsible for scaling Fetcherr's data-driven products and engineering capabilities. This hybrid role combines technical authority, strategic vision, and operational excellence.
Key responsibilities include:
Strategic & Technical Leadership: Act as a senior technical authority in data architecture, analytics, monitoring, and system design. Report to the CTO and collaborate with senior peers to define and execute the company's technical roadmap. Drive execution, innovation, process improvement, and change management across engineering and data science teams. Scale the R&D team in line with rapid growth. Champion the effective usage of state-of-the-art AI tools for improving productivity and scaling team capabilities.
Engineering & Operational Excellence: Design and refine engineering processes to improve reliability, velocity, and quality. Champion data quality, validation, and analytics engineering practices. Deliver commitments according to customer requirements and timelines. Drive productivity and quality through best-practice leverage of AI tools and agentic coding.
Customer & Product Alignment: Serve as a technical interface to product management, understanding customer needs and translating them into product development execution and technical strategies. Represent R&D in customer discussions, guiding prioritization and product development with a real-world impact lens.
People & Culture: Lead a local team while collaborating with remote and distributed teams, fostering alignment, engagement, and accountability. Promote a collaborative, analytical, data-driven, hands-on culture across engineering, data science, and product. Partner closely with product and data stakeholders to deliver scalable, production-grade AI solutions.
