Senior Software Engineer

• Lead moderately complex initiatives and deliverables within technical domain environments
• Contribute to large scale planning of strategies
• Design, code, test, debug, and document for projects and programs associated with technology domain, including upgrades and deployments
• Review moderately complex technical challenges that require an in-depth evaluation of technologies and procedures
• Resolve moderately complex issues and lead a team to meet existing client needs or potential new clients needs while leveraging solid understanding of the function, policies, procedures, or compliance requirements
• Collaborate and consult with peers, colleagues, and mid-level managers to resolve technical challenges and achieve goals
• Lead projects and act as an escalation point, provide guidance and direction to less experienced staff

• 4+ years of Software Engineering experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education
• Own end‑to‑end performance engineering activities including workload modeling, test planning, execution, analysis, and reporting
• Design and execute load, stress, endurance, spike, and volume testing using LoadRunner and JMeter
• Perform full‑stack performance diagnostics across application, JVM, database, middleware, and infrastructure layers
• Conduct heap and thread dump analysis, GC log reviews, CPU/memory profiling, and drive tuning and optimization
• Act as a performance gatekeeper for releases, ensuring SLAs, non‑functional requirements, and production readiness standards are met
• Utilize AppDynamics, Dynatrace, Splunk, Grafana, Prometheus, and Elastic APM for monitoring, anomaly detection, trend analysis, and root‑cause investigation
• Develop Python‑based automation utilities to streamline performance data analysis, log parsing, reporting, and efficiency improvements
• Apply AI/ML concepts for anomaly detection, predictive performance trend analysis, and proactive capacity planning
• Optimize relational databases and MongoDB through query tuning, indexing, and schema optimization
• Support performance validation in cloud‑native and containerized environments, including OpenShift/Kubernetes
• Integrate performance validation into CI/CD pipelines and leverage service virtualization to stabilize test environments
• Collaborate with architects, developers, SREs, and platform teams
• participate in design reviews to embed performance early in the SDLC
• Support production incidents, perform root‑cause analysis, and document outcomes using JIRA and Confluence
• Strong hands‑on experience with LoadRunner and JMeter/BlazeMeter
• Proven expertise in application performance analysis and optimization
• Advanced Unix/Linux troubleshooting skills
• Strong understanding of API and microservices‑based architectures
• Hands‑on experience with monitoring and observability tools (AppDynamics, Dynatrace, Splunk, Grafana, Prometheus, Elastic APM)
• Should have exposure in end-to-end stages of performance testing life cycle
• Good to have knowledge in Python scripting and automation
• Experience with cloud platforms and container technologies (OpenShift/Kubernetes)
• Strong knowledge of JVM tuning, memory management, and garbage collection
• Hands‑on experience with MongoDB performance tuning
• Familiarity with CI/CD pipelines, service virtualization, and DevOps practices
• Understanding of AI/ML concepts applied to performance engineering and observability
• Strong analytical, problem‑solving, and documentation skills
• Excellent communication skills with the ability to drive clarity across teams
• Continuous learning mindset with passion for modern engineering practices

Good to have knowledge in Python scripting and automation