Senior Platform Engineer (Python/AWS/CI-CD/Microservices)
Budget: -
HOURLY / FULL_TIME
⭐ 4.86 (164)
United States
docker, cicd, aws-lambda, cicd-platforms
Preferred qualifications
- Location: Europe, Americas
- Experience: Expert
NO AGENCIES. INDEPENDENT FREELANCERS ONLY.
READ THIS FIRST: We need someone who has built CI/CD pipelines and AWS infrastructure from scratch and carried the pager for what they built. If your AWS experience is limited to using pipelines and environments someone else set up, do not apply. This will be tested during screening.
You will own the AWS environment the program runs in: deployments, monitoring, incident response, and the existing microservices estate, while the migration reshapes what runs on it. Full-time, long-term engagement.
Key Responsibilities:
• Deploy, monitor and support Python services running in AWS; own alerting and incident response for production integrations
• Build and maintain CI/CD pipelines, Docker-based deployment, and infrastructure automation
• Maintain and enhance the existing microservices and integrations
• Troubleshoot production integrations and data flows
• Build the reusable integration framework the team standardizes on
• Drive observability: logging, metrics, tracing and dashboards across services
• Manage secrets, IAM policies and environment controls
• Design rollback-ready, zero-downtime deployments
Requirements:
• 6+ years of backend engineering with strong AWS depth (ECS/EKS, Lambda, API Gateway, SQS/SNS, CloudWatch or equivalents)
• Strong Python in production (will be tested during screening)
• Docker and containerized application delivery
• CI/CD ownership: you have built pipelines, not just used them
• Infrastructure as code: Terraform, CDK or CloudFormation
• Production operations experience: on-call, incident response, postmortems
• SQL working knowledge
• Fluent English
• Minimum 4 hours daily overlap with US Eastern time, full-time availability
Nice to Have:
• Event-driven architecture at scale (queues, dead-letter handling, replay)
• Cost optimization of AWS workloads
• Shopify, POS or OMS platform exposure
• BigQuery or data pipeline tooling
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