Locals only - Sr .NET Performance/Support Engineer - Grand Rapids, MI (Fully Onsite) - CG

Greetings for the day !
I hope you are doing well.

My name is Ayush, and I am a Sr. Technical Recruiter at Empower Professionals Inc. I wanted to reach out regarding a "Senior .NET Performance Engineer" role with one of our clients based out in Grand Rapids, MI (Onsite).

Please let me know if you are available in the market and interested in this role (see job description below) - if so, we can connect and speak further.

Role: Senior .NET Performance Engineer Duration: 12+ Months
Location: Grand Rapids, MI (Fully Onsite)

Note: This is not a Developer role (Need Observability, Monitoring, APM, .Net Support and Engineering)

Job Description:

• 7+ years building/supporting .NET applications (.NET Framework and .NET 6/ 7/ 8).
• Strong knowledge of CLR internals, modes, thread pool behavior, JIT and Async/Await execution.
• Experience with runtime profiling, trace/dump analysis, algorithmic review and diagnostic instrumentation.
• Hands-on experience with Azure Performance Profiling, Azure Monitor, Application insights and cloud-hosted performance considerations.
• Proficiency with APM/ observability tools such as Dynatrace, Datadog, Grafana or similar.
• Familiarity with distributed tracing (e.g., OpenTelemetry) and multi-service trace interpretation.
• Diagnose performance issues across .NET apps running in-cloud, on-prem, and hybrid.
• Analyze CPU usage, memory behavior, Async execution, concurrency, data-access patterns and dependency latency.
• Define service-level expectations for latency, throughput, and resource utilization.
• Design and run load, stress, and soak tests that reflect actual traffic and seasonal patterns.
• Review distributed traces, flame graphs, and telemetry to find bottlenecks across APIs, caches, messaging, and data layers.
• Build dashboards and alerts that highlight KPIs and catch regressions early.
• Provide performance-focused input during design and architecture reviews.
• Work with developers to improve algorithmic efficiency, concurrency patterns and resource usage.
• Identify inefficient coding patterns that cause memory pressure, blocking behavior or excessive overhead.
• Create documentation, playbooks and best practices for profiling and performance tuning.
• Mentor teams on performance-aware development and interpreting performance data.