Senior Backend Engineer, Platform Enablement
New
J
JobgetherSecurity & IT
Fully remote position within the United States.Full-TimeSenior
SalaryBase salary range of $156,800–$235,200 USD.
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Job Details
- Required Skills
- AWSGCPKubernetes
Requirements
- Demonstrated experience building backend or platform software and developing internal platforms or developer-facing tooling.
- Experience partnering with software development teams to improve engineering workflows, productivity, and operational practices.
- Experience applying AI-assisted development practices across the software lifecycle, including implementation, testing, debugging, documentation, or automation.
- Practical experience with software performance, such as performance analysis, optimization, benchmarking, or leading performance-related engineering improvements.
- Hands-on experience with Kubernetes and k6, along with Amazon Web Services, Google Cloud Platform, or comparable cloud infrastructure.
- Ability to design durable, reusable solutions that support broad adoption through strong systems thinking.
- Strong backend engineering and platform design skills, with an understanding of scalable developer infrastructure and self-service capabilities.
- Clear written and verbal communication skills, with the ability to influence and collaborate effectively across teams without relying on direct authority.
- Ability to work effectively in an asynchronous environment and collaborate broadly across research and development organizations.
- Strong problem-solving skills and the ability to turn complex or ambiguous performance challenges into practical, scalable engineering solutions.
Responsibilities
- Build and continuously evolve reusable backend platforms and developer tooling that support performance testing, analysis, benchmarking, and optimization.
- Create self-service workflows, documentation, templates, diagnostics, and AI-assisted engineering capabilities that reduce reliance on bespoke performance support.
- Enable feature teams to integrate performance testing into their own development and continuous integration workflows.
- Apply AI-enabled development practices to accelerate implementation, testing, debugging, documentation, and engineering automation.
- Improve performance results, reporting, trend visibility, and evidence-based decision-making so engineering teams can evaluate performance independently.
- Help establish the documentation, templates, standards, and guidance required to expand self-service adoption across research and development.
- Influence engineering teams to make performance testing and analysis an integral part of development and release preparation.
- Turn hands-on engagements and recurring performance challenges into generalized capabilities that can be adopted by multiple teams.
- Provide technical expertise for complex performance questions, tooling improvements, and new platform capabilities.
- Collaborate asynchronously and cross-functionally with development teams to build solutions that scale beyond individual engagements.
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