Senior DevOps Engineer (GCP)
100% REMOTE - Senior Performance Engineer Summary The Senior Performance Engineer is a technical leader responsible for ensuring that large-scale, mission-critical applications-particularly mortgage processing, document automation, BPM workflows, and rules engines-meet stringent performance, scalability, and resiliency goals. This role requires deep expertise in performance engineering, distributed systems, cloud-native architectures, and AI driven analysis. The engineer will lead performance strategy, design and execute complex test scenarios, analyze system behavior across layers, and influence architectural decisions to deliver high-performing, scalable solutions across hybrid cloud environments. MUST HAVE: Perforemance Engineering experience (resolving issues after test) LoadRunner ENTERPRISE JMeter AWS experience (including Lambda, ECS) Backgroun in Java Essential Job Duties and Responsibilities Performance Engineering Lead end-to-end performance engineering activities-from requirement gathering and workload modeling to analysis, diagnosis, and optimization. Define performance KPIs, SLAs, SLOs, and non-functional requirements for enterprise applications. Mentor engineers, provide technical leadership, and drive performance engineering best practices across teams. Cloud Native Performance Engineering Design and execute performance tests for cloud-hosted, microservices-based, and containerized applications (AWS/Azure/Google Cloud Platform). Evaluate autoscaling policies, load-balancer behavior, API gateway performance, and cloud-native distributed workloads. Analyze cloud cost-performance tradeoffs and optimize system efficiency across compute, storage, and network layers. Use cloud-native observability platforms (CloudWatch, Azure Monitor, Google Cloud Platform Operations Suite) to collect and interpret performance telemetry. AI Driven Performance Optimization Apply AI/ML-based techniques to detect anomalies, predict bottlenecks, and identify performance degradation patterns. Use AI-enabled analytics tools to accelerate root-cause analysis and capacity forecasting. Integrate intelligent insights into dashboards, thresholding models, and performance reports. Performance Testing & Automation Develop performance scripts using LoadRunner, JMeter, Gatling, Locust, K6, or equivalent frameworks. Automate performance pipelines via CI/CD tools (Jenkins, GitHub Actions, Azure DevOps). Build reusable, modular workloads to reflect real-world traffic patterns, data variations, and tenant behavior. Performance Monitoring & Observability Utilize APM tools (Dynatrace, AppDynamics, New Relic, Datadog, Grafana) to monitor system behavior in real time. Define observability standards including telemetry instrumentation, distributed tracing, and structured logging. Develop dashboards that correlate performance metrics, traces, logs, and infrastructure KPIs. Advanced Analysis & System Optimization Perform deep-dive performance diagnostics across application, database, messaging queues, caching layers, and integrations. Recommend tuning strategies for JVM/.NET, APIs, microservices, databases (SQL/NoSQL), and cloud resources. Evaluate resilience and scalability through chaos testing, failover tests, and peak readiness assessments. Collaboration, Governance & SDLC Integration Partner with engineering, architecture, DevOps, SRE, QA, and product teams to ensure performance is built into design, not tested at the end. Drive non-functional requirements early in the SDLC via architecture reviews and design assessments. Maintain detailed performance documentation including baselines, reports, RCA documents, tuning recommendations, and capacity models. Additional Responsibilities Identify, document, and track performance defects through JIRA. Stay current with evolving performance tools, cloud technologies, and AI/ML trends. Maintain regular and punctual attendance and perform related duties as assigned. Supervisory Responsibilities This is an individual contributor role with leadership responsibilities across technical domains and cross-functional collaboration, but no direct reports.
Qualifications
Expert-level understanding of performance engineering principles, distributed systems, concurrency models, and cloud-native architectures. Strong analytical and diagnostic skills with the ability to interpret complex performance data. Proven ability to influence architecture and design decisions based on performance insights. Experience with AI/ML-based observability, anomaly detection, or predictive analytics is highly desirable. Knowledge of mortgage industry workflows, document automation, and rules engines is a plus. Education and/or Experience Bachelor's degree in Computer Science, Engineering, or related field. 10-18 years of progressive experience in performance engineering and large-scale system optimization. Hands-on expertise with performance testing tools (LoadRunner, JMeter, Gatling, Locust, K6, etc.). Strong experience with cloud environments (AWS, Azure, or Google Cloud Platform). Solid understanding of databases (MongoDB, PostgreSQL, SQL Server, or NoSQL stores) from a performance standpoint. Experience with APM/observability platforms and log analysis tools. Experience integrating performance testing into CI/CD ecosystems. For applications and inquiries, contact: [email protected] Apply tot his job Apply To this Job