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Mulesoft Developer[REMOTE]

Columbus, OH · On-site

Teams Remote position Responsibilities: 1. Ability in implementing Mulerecommended best practices ... Intermediatelevel Java, Python or other modern-day language development experience is aplus. 4.

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How much do python backend developer remote jobs pay per year?

As of Aug 20, 2026, the average yearly pay for python backend developer remote in the United States is $148,233.00, according to ZipRecruiter salary data. Most workers in this role earn between $145,500.00 and $167,500.00 per year, depending on experience, location, and employer.

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Python Backend Developer Remote jobs near you
A map of the United States highlighting the number of Python Backend Developer Remote job openings by state according to ZipRecruiter. The image is accompanied by a detailed chart listing the number of Python Backend Developer Remote job openings in each state, with California having the most at 2 and Hawaii the least at 0.

Staff Software Engineer (backend) Columbus HQ / Remote

Motion LLC

Columbus, OH • On-site

$120 - $160/hr

Other

Posted 2 days ago

New

Job description

Staff Software Engineer (backend) Full Time Columbus HQ / Remote

As the Staff Software Engineer and right-hand to the CTO, you will own the architectural integrity of our distributed backend. This is a high-autonomy role designed for a systems-thinker who wants to influence every layer of a complex, hybrid environment.

Technical Requirements 1. Expert Backend Engineering
  • 10+ Years of Experience: A proven track record of building and scaling production systems using Go (Golang) and other backend-focused languages.
  • Microservices & Distributed Systems: Deep expertise in designing decoupled architectures that handle high concurrency, partial failures, and eventual consistency.
  • Public API Design: Experience building and versioning robust Public APIs that are consumed by both internal systems and external third-party partners.
  • Advanced Authorization: Practical experience with relationship-based access control (ReBAC). Familiarity with OpenFGA or Google Zanzibar-style models is highly preferred.
2. Resilient Cloud & Infrastructure
  • High Availability & Multi-AZ: Experience architecting multi-AZ deployments on AWS to ensure that cloud-level outages do not disrupt service.
  • Infrastructure as Code (IaC): Mastery of Terraform for managing scalable, repeatable AWS environments.
  • Portable CI/CD: A platform-agnostic approach to automation. You understand how to build pipelines (currently GitHub Actions) that are robust and migratable.
  • Orchestration: Familiarity with Kubernetes, AWS ECS, or other cluster management tools.
3. Edge & Systems Mastery
  • Linux Systems & Shell Scripting: Expert-level comfort with Ubuntu. You can write production-grade Bash scripts that manage system-level tasks within containerized environments.
  • Docker at the Edge: Expertise in bundling multiple Go binaries and scripts into optimized Docker images for remote execution.
  • Custom Orchestration: Experience (or a high degree of comfort) managing pipelines to push updates to field hardware in low-bandwidth or intermittent-connectivity environments.
Key Responsibilities
  • Strategic Partnership: Act as the CTO’s primary technical advisor, vetting new technologies and turning the product vision into a technical roadmap.
  • System Ownership: Take full ownership of the data flow from field-deployed sensors to the cloud, ensuring high performance in our PostgreSQL and Redis layers.
  • Architectural Mentorship: Set the "gold standard" for code quality, testing strategies, and documentation across the backend team.
  • Cross-Environment Reliability: Bridge the gap between standard cloud CI/CD and custom edge update mechanisms to ensure the entire fleet stays synchronized.
Nice-to-Haves (Training Provided)

We prioritize core systems engineering and Go mastery. We are happy to provide training on the domain-specific protocols and emerging tech below:

  • Industrial Protocols: BACnet and Modbus for hardware communication.
  • Wireless Systems: LoRaWAN or similar low-power wide-area networks.
  • AI & Future Tech: Integrating LLMs and building RAG (Retrieval-Augmented Generation) agents using vector-based search.
  • Secondary Languages: Familiarity with Python for data-heavy or AI-adjacent logic.
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