Job Description

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.

The opportunity

Our rapid growth into new verticals and markets presents a fantastic opportunity to evolve our platform. We are building on our initial successes by transitioning to a highly scalable architecture that seamlessly supports our expanding customer base across diverse industries.

It’s an engineering opportunity to turn a carefully operated platform into a Kubernetes-native, self-service product. The goal is to serve internal customers first and deliver at high velocity with safe, capable self-service solutions aligned with the best practices of platform engineering.

It's a leadership opportunity in equal measure: you'll build and grow the team that owns it, and have an impact on how platform engineering is done here as the company scales.

What you'll own

• Self-Service Enablement: Empower cross-functional teams with secure, automated pathways that accelerate development while maintaining organizational standards.
• Architectural Modularization: Evolve our infrastructure into a decoupled, scalable architecture that supports rapid iteration and independent service lifecycles.
• Unified Infrastructure Management: Implement a rigorous "Everything as Code" strategy, treating infrastructure, policy, and configuration with the same standards as application software.
• Proactive Reliability: Foster a culture of operational excellence through robust observability, clear Service Level Objectives, high operational hygiene and safety.

What success looks like:

• In 6 months: Lead the transformation to a self-service infrastructure platform. Target a 30% reduction in deployment lead times and a 50% increase in self-service adoption among R&D teams. Implement platform-wide observability and SLO monitoring to ensure 99.9% uptime for core services.
• In 12 months: Evolve the Platform Engineering team into a high-scale, autonomous unit that sets the standard for engineering excellence. Define technical domain ownership for core infrastructure and scale the global team.

Who you are as a leader:

• Your job is to deliver value sustainably — at a pace the team can hold indefinitely, not a sprint to burnout.
• You empower, you don't assign. You give people context and autonomy, hold them accountable, and speak less so they speak more.
• You build a learning team — retrospectives that turn data into action, postmortems that find root cause without blame, and deliberate work on growing each engineer and killing the bus-factor-of-one.
• "The how" is non-negotiable: tested, reviewed, observable, highly available, staging-first. "Done" means in production, serving customers.
• You stay hands-on enough to earn technical trust and make hard calls.

Requirements

What you bring

• A strong record in Platform Engineering / DevOps, with deep hands-on Kubernetes in production.
• Proven experience leading engineers: mentoring, hiring, and growing a team
• Strong cloud-native and Kubernetes-native mindset: you reach for declarative, GitOps, controllers, and abstraction before bespoke scripts.
• Fluency in Python, Go, and Bash sufficient to build tooling and review your team's work.
• Hands-on with multi cloud at production scale (GKE, Cloud SQL, BigQuery).
• Fluency in Infrastructure as Code (Terraform, KCC), Helm, and GitOps delivery (ArgoCD or similar) and opinions on doing them well at scale.
• A product mindset toward platform: you treat internal engineers as customers and self-service as the goal.
• A point of view on AI-assisted and spec-driven development, and how to make engineers faster with it while keeping change safe to ship.
• A culture of learning from failure: blameless postmortems, real root cause, and fixing the system so the same incident can't recur.

Nice to have

• Multi-cloud experience (AWS / Azure) — GCP is home, but breadth helps.
• Policy-as-code (OPA/Conftest, Kyverno) and supply-chain/CI security.
• SLO/error-budget practice and DORA-based delivery measurement.
• Big Data or MLOps exposure (Airflow, Dagster, Ray, Kubeflow).