Software Engineer - Engineering Productivity
New
Based in the United StatesFull-TimeSenior
Salary$146,200 to $190,000 USD
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Job Details
- Experience
- 5+ years
- Required Skills
- AWSPythonGCPGoCI/CDDistributed Systems
Requirements
- 5+ years of professional software engineering experience with strong proficiency in backend development using languages such as Python, Go, or similar technologies.
- Proven experience designing, maintaining, and improving distributed systems, backend infrastructure, and engineering productivity platforms.
- Strong expertise with Infrastructure as Code (IaC), cloud platforms such as AWS and/or Google Cloud Platform (GCP), and modern CI/CD practices.
- Hands-on experience implementing automated testing, performance testing, security validation, and software quality engineering solutions.
- Familiarity with AI-assisted software development tools and the ability to safely integrate AI-generated code and infrastructure into engineering workflows.
- Strong understanding of software reliability principles, engineering metrics, observability, and platform performance optimization.
- Excellent communication, collaboration, and stakeholder management skills, with the ability to influence engineering teams and drive organizational adoption of technical initiatives.
- Product-oriented mindset with a focus on delivering measurable business outcomes, continuous improvement, and high-quality engineering practices.
Responsibilities
- Design and drive engineering-wide reliability programs that improve software quality and deployment confidence across development teams.
- Build and enhance automated testing frameworks, CI/CD pipeline gates, and quality controls covering performance, security, accessibility, and reliability.
- Define, monitor, and improve engineering metrics such as DORA metrics, SLIs/SLOs, deployment quality indicators, and platform health measurements.
- Develop scalable load testing solutions, synthetic data architectures, and production validation strategies that enable safe, high-confidence releases.
- Leverage AI-assisted development tools to accelerate testing infrastructure, automation, and engineering productivity while maintaining rigorous validation standards.
- Lead modernization efforts by consolidating legacy testing platforms into unified, automated engineering solutions.
- Collaborate closely with engineering teams to improve development practices, reduce operational overhead, document technical solutions, and promote consistent software quality across the organization.
- Act as a technical leader and trusted partner, influencing engineering standards and driving adoption of reliability-focused processes throughout the software development lifecycle.
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