Spreetail

Senior Platform Engineer

Job Description

As a Senior Platform Engineer, you’ll be a technical leader and force multiplier. You will architect and evolve the internal platforms that power innovation at Spreetail. You won’t just maintain infrastructure — you will design the golden paths, AI-assisted workflows, and platform guardrails that enable hundreds of engineers and business builders to ship safely and quickly. This role blends cloud platform architecture, infrastructure automation, data platform design, and AI-assisted development enablement. You don’t need to be an ML researcher — but you must be excited about turning AI from a demo into a dependable engineering capability. You will help define how software gets built at Spreetail. 


How you will achieve success:
  • Architect Platform Golden Paths: Design and evolve secure, scalable AWS-based golden paths (infra, CI/CD, data, AI) that become the default way teams build and deploy services. 
  • Operationalize AI in the SDLC: Integrate AI-assisted code generation, refactoring, migration, and validation workflows into CI/CD and developer tooling in ways that measurably reduce cycle time and rework. 
  • Design Safe Abstractions Over Complexity: Build secure abstractions over Kubernetes, IAM, networking, and data systems so builders can move fast without deep infrastructure knowledge. 
  • Drive Reliability, Observability & Cost Efficiency: Establish platform guardrails and monitoring patterns that scale quality instead of slowing velocity. 
  • Multiply Engineering Impact: Lead cross-team initiatives, mentor SE II/III engineers, and elevate platform standards across DevEx, AI Ops, and infrastructure domains. 

    • What experiences will help you in this role:
  • 7–10+ years of experience building and operating production systems, with strong hands-on expertise in AWS and Terraform-based infrastructure. 
  • Proven experience designing Kubernetes and/or serverless architectures that balance speed, safety, and scalability. 
  • Experience building or evolving internal platforms, developer tooling, or golden paths used by multiple engineering teams. 
  • Strong understanding of modern data platforms (S3, SQL, Iceberg, data lake patterns) and how infrastructure supports experimentation and scale. 
  • Experience integrating AI into real engineering workflows (e.g., code generation, migration automation, testing, CI validation, internal copilots, or AI Ops patterns). 

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