Staff Software Engineer - Full Stack (Performance Engineering) (Hybrid/Onsite) - job 14 of 20

The Staff Software Engineer - Full Stack (Performance Engineering) at Open VisaNet will primarily focus on performance plans and models for scalable infrastructures. This includes conducting chaos engineering experiments, producing load-testing tools, and executing performance tuning for payment processing systems. You’ll engage in rigorous collaboration with various teams to refine application performance while ensuring high-availability and resiliency.

Candidates for the Staff Software Engineer - Full Stack (Performance Engineering) role at Open VisaNet should possess an in-depth understanding of application performance KPIs, alongside strong skills in performance modeling, chaos testing, and a solid grasp of microservices architecture. Proficiency in programming languages such as Golang or Python is essential, along with familiarity with Linux systems and performance optimization techniques.

At Open VisaNet, the work environment for the Staff Software Engineer - Full Stack (Performance Engineering) is hybrid, where employees alternate between working from home and in-office. This setup encourages flexibility and collaboration, allowing you to engage with your team in meaningful ways while also maintaining a work-life balance.

Open VisaNet offers numerous growth opportunities for the Staff Software Engineer - Full Stack (Performance Engineering) to explore, including professional development through hands-on projects, exposure to cutting-edge technology, and the chance to lead initiatives that impact application performance significantly. You will also have the opportunity to mentor junior engineers, enhancing your leadership skills.

Open VisaNet fosters a culture of innovation by encouraging team members in the Staff Software Engineer - Full Stack (Performance Engineering) position to explore new ideas, conduct experiments, and share insights on improving system performance. Regular collaboration and knowledge-sharing sessions allow for continuous learning and the implementation of innovative solutions.

In performance engineering for a microservices architecture, I would start by identifying key performance indicators (KPIs) for each service. I would then simulate realistic load conditions using appropriate testing tools, monitor performance metrics, and analyze results to pinpoint bottlenecks. Finally, I would implement optimizations and document findings to ensure continuous improvement.

To conduct a root cause analysis, I would begin by gathering performance metrics and logs to identify anomalies. Then, I would engage in a systematic review of the application's architecture, configuration, and code to pinpoint the source of the problem. Collaboration with development and operations teams is crucial, and I would ensure findings lead to actionable insights.

For performance testing, I typically use tools like JMeter and Gatling due to their robust capabilities in load testing and monitoring. These tools provide valuable insights into response times, throughput, and scalability. Additionally, I leverage tools like Prometheus for real-time monitoring to help optimize performance under peak loads.

In a previous role, I identified high latency issues in a payment processing application. I implemented performance profiling to detect slow queries and analyzed network bottlenecks, leading to optimized database indexing and improved API response times. As a result, overall system performance increased by 30%, enhancing user experience significantly.

I approach chaos engineering by designing hypotheses around potential system failures and systematically introducing faults into the application environment. This helps me observe system behavior under stress and identify vulnerabilities in microservices. Based on findings, I collate strategies to enhance resilience and ensure robust service continuity.

I have extensive experience with load testing tools like LoadRunner and k6. I have applied these tools in various projects to simulate high-traffic scenarios, evaluate system performance under different conditions, and identify the application's breaking point, allowing us to make necessary optimizations prior to launch.

Establishing performance baselines is critical as it provides a reference point for measuring the impact of changes or enhancements in the application. Baselines enable the identification of performance regressions and help maintain consistent user experience, ensuring the application meets the required performance standards over time.

I ensure documentation of performance engineering processes by creating comprehensive reports that outline methodologies, test cases, results, and lessons learned from experiments. This documentation not only serves as a valuable resource for future projects but also promotes knowledge sharing within the team and aids in maintaining best practices.

To prevent performance issues, I advocate for a systematic approach where performance considerations are integrated early in the development lifecycle. This includes establishing performance benchmarks, conducting regular code reviews focused on efficiency, and engaging in collaborative performance testing sessions with development teams.

I stay updated with the latest trends in performance engineering by participating in online forums, attending industry conferences, and following thought leaders in the field. Additionally, I actively engage in webinars and training sessions, ensuring that I can apply cutting-edge techniques and tools in my work.