Software Engineer Intern - Data Infrastructure and Tools
- Design and Implement Monitoring Systems: Build out and maintain continuous automated monitoring pipelines for crucial system performance characteristics, including node start times, system-critical code sections, and end-to-end latency metrics.
- CI/CD Integration: Integrate performance tracking into release branch Continuous Integration (CI) environments and build on-demand testing capabilities for GitHub/GitLab Pull Requests.
- Automate Alerting: Develop mechanisms to automatically detect, flag, and escalate performance outliers and regressions in real-time.
- Data Visualization & Trend Analysis: Create systems or dashboards to track and visualize overall performance trends based on specific variables such as workload and geographic location.
- Bridge the Gap: Transition the team from manual, "human-eye" performance checks to a fully automated data pipeline that makes the best use of both production and release candidate CI data.
- Python Proficiency: Strong, hands-on programming experience with Python for backend development and automation.
- Backend & Infrastructure Fundamentals: Solid understanding of backend software development principles, data structures, and system architecture.
- CI/CD Knowledge: Foundational understanding of Continuous Integration workflows, version control (Git), and automated testing principles.
- Analytical Mindset: Strong problem-solving skills with a keen interest in analyzing system metrics, identifying bottlenecks, and working with data infrastructure.
- JavaScript Experience: Basic knowledge of or hands-on experience with JavaScript (helpful for frontend dashboarding or data visualization tweaks).
- Familiarity with CI Tools: Prior exposure to industry-standard CI/CD platforms (e.g., Jenkins, GitHub Actions, GitLab CI, Buildkite).
- Observability & Monitoring: Experience with or an understanding of performance monitoring, logging, and visualization tools (e.g., Grafana, Prometheus, Datadog, ELK stack).
- Edge/Hardware Context: An understanding of distributed systems, IoT, or edge computing environments (helpful for understanding performance metrics generated "on trucks").
- Data Handling: Experience working with APIs, processing large datasets, or managing data pipelines.
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