The Cost of Pretending: How Fake Performance Engineers Are Costing Businesses

This topic hit me during a casual chat with a friend and I know it would ruffle some feathers. But hey, someone had to talk about the elephant in the data center: fake performance engineers are slowing down real business outcomes. If you're faking it, this might sting. If you're not — you'll get it.

In the world of software delivery, performance engineering is often hailed as the silent guardian of user experience — identifying bottlenecks, optimizing resource usage, and ensuring scalable systems. Yet ironically, the field itself is now riddled with a growing bottleneck of its own: fake expertise.

As digital transformations accelerate and businesses modernize legacy platforms, many firms expect their performance engineers to be the torchbearers of reliability and speed. But behind the charts and dashboards, an uncomfortable truth lurks — a sizable portion of the talent pool may lack the depth they claim to have. And this isn't just an HR issue — it's a technical risk.

The Rise of “Resume Engineers”

During the pandemic, remote hiring opened global opportunities but also paved the way for dishonest practices:

• Proxy interviews using so called "subject-matter experts"

• Exaggerated resumes loaded with keywords and tools

• Certification mills and pre-fed “project stories”

In performance engineering — where tools like JMeter, LoadRunner, Gatling, or Dynatrace are easy to mention but hard to master — the gap between knowing the tool and knowing the craft is stark. Unfortunately, it's also easily hidden in superficial interviews.

Symptoms of the Problem

Many product teams start noticing something’s off only after a critical performance gate is missed. Common red flags include:

• Reluctance to test without a “final stable build”

• Blaming missing NFRs instead of collaborating to define them

• Producing trivial test results with no insights

• Avoiding root cause analysis or multi-layer debugging

• Inability to work with observability tools or interpret data beyond charts

These aren’t just signs of inexperience — they reflect a lack of foundational understanding. And in high-stakes environments, that’s dangerous.

How This Hurts Organizations

1. False Sense of Readiness: Ineffective tests produce green reports that hide real issues — until the app crashes in production.

2. Wasted Modernization Efforts: Migration from legacy to modern platforms often fails to meet performance goals due to shallow validation.

3. Demoralized Product Teams: Developers lose trust in performance feedback when engineers can't offer actionable suggestions.

4. Stunted Innovation: Teams become hesitant to aim higher — fearing unknown risks that go unmeasured due to lack of capable evaluators.

The damage isn’t limited to timelines. It’s cultural. When teams start accepting poor performance practices as “normal,” they stop striving for excellence.

Not Just a Performance Problem — A Tech Industry Issue

While performance engineering is particularly affected due to its complexity and ambiguity, this isn’t isolated. From DevOps engineers who can’t script to data analysts who can’t query — nearly every tech domain today grapples with inflated credentials.

Why? Because:

• Interview pressure encourages buzzword-stuffing

• Companies prioritize rapid hiring over rigorous vetting

• Skill assessments are often limited to tool familiarity, not system-level thinking
  

Fixing the Leaks — What Can We Do?

1. Audit the Outputs, Not Just the Resumes
   Evaluate performance engineers based on how they think, not just the tools they list. Look for:

   • Test design approach

   • Debugging methodology

   • Analysis of a failed test

2. Encourage Cross-Disciplinary Learning
   Good performance engineers understand the system end-to-end. Encourage collaboration with backend, infra, and observability teams.

3. Mentor Instead of Just Replace
   Not every underperformer is a fraud — some are undertrained. Create safe learning spaces to uplevel talent.

4. Build Interview Rigor
   Move beyond scripted questions. Ask candidates to:

   • Analyze a real test result

   • Design a performance test strategy

   • Walk through their troubleshooting thought process

The goal of performance engineering isn’t just “acceptable speed.” It’s confident scalability. It’s engineering trust. But that trust erodes when the people meant to protect it are unqualified or inauthentic. The community — from hiring managers to tech leads, needs to acknowledge this quietly growing issue. It’s time we raised the bar, asked better questions, and made space for real talent to rise.

Because if performance engineering is to truly enable bold, resilient systems, we must first debug our own community.