Senior Associate - Engineering as a Service/Operate

Job Summary:

As Sr Product Software Engineer I, Reliability Platforms, you will build and own services within the traffic, compute, and streaming platform layers production systems, and develop the reliability tooling that keeps those systems dependable at global scale.

You will be part of an organization made up of three complementary teams.

• Technical Operations is responsible for production readiness and service onboarding, alert response and first-line eyes-on-glass monitoring, defining alerts and thresholds, escalation management, backend capacity management and load testing, and third-layer support for technically complex customer issues.
• Reliability Platforms is responsible for the traffic, compute, and streaming platform layers, and for the reliability tooling that engineers reliability, resiliency, and self-healing into our production services and automates operational processes.
• Observability & Platform is responsible for telemetry, instrumentation, and alerting standards, for the internal self-service tooling and automation platforms that enable teams across the organization to deploy, detect, alert, and mitigate in our AWS environments with reliability and velocity, and for the operational data, reliability reporting, and anomaly detection that make those platforms smarter over time.

You must understand the demands of managing 24x7 applications at a large scale with well-defined operational objectives. You bring hands-on expertise across AWS, systems programming, the software development lifecycle, incident response and management, and observability tooling such as Datadog, CloudWatch, and Grafana. Within Reliability Platforms you will own services end to end, mentor engineers, take technical direction from the Lead Product Software Engineer, and partner with Technical Operations to automate away operational toil. You will report to the Director, Product Software Engineering.

Responsibilities and Duties of the Role:

• Design, build, and own services and automation within the traffic, compute, and streaming platform layers, engineering reliability, resiliency, and self-healing behavior into production services
• Implement and improve SLO measurement, error budget reporting, and reliability tooling used by engineering teams
• Build and maintain safe deployment pipelines using reliable deployment strategies such as canary and blue/green
• Contribute to automated testing coverage for platform services, including unit, integration, performance, and chaos testing
• Participate in the on-call rotation and lead incident response and blameless post-incident reviews for owned services
• Mentor engineers, contribute to design and code review practices, and partner with Technical Operations to automate away operational toil

Required Education, Experience/Skills/Training:

Basic Qualifications:

• 5+ years of professional software engineering experience with large-scale, distributed, highly reliable systems, including their development, operations, and on-call support
• Experience with Java, Python, Rust, Go, or other systems programming languages; you write production-quality code, not just scripts
• Hands-on experience with Terraform, Kubernetes, Docker, and immutable infrastructure patterns
• Experience deploying and running AWS-based solutions and familiarity with tools such as IAM, CloudWatch, EC2, EKS, Kinesis, DynamoDB, ElastiCache, Lambda, and S3
• Experience with CI/CD tooling such as Jenkins, GitHub Actions, GitLab CI, or Spinnaker, and reliable deployment strategies such as canary and blue/green
• Experience with automated testing at different levels, including unit, integration, functional, and performance testing
• Experience with observability tooling such as Datadog, Prometheus, CloudWatch, or Grafana, including instrumenting services and tuning alerts
• Clear, succinct written and verbal communication with technical and non-technical stakeholders, and a practical, collaborative approach when working with engineers

Preferred Qualifications:

• Experience with data, streaming data, and analytics ecosystems such as Kafka, Flink, or Kinesis
• Experience with chaos testing frameworks and resilience engineering practices
• Experience developing APIs and designing database schemas
• Experience working with GenAI, LLMs, or agents, and leveraging AI as a productivity multiplier
• Experience with capacity management, load testing, and performance tuning for high-traffic services
• AWS certifications

Required Education:

• Bachelor's degree in Computer Science, Information Systems, Software, Electrical or Electronics Engineering, or a comparable field of study, or equivalent training or work experience