The Big Tech Transplant
Background: Senior IC or manager from FAANG, first startup role
Impressive resume. Deep technical skills. But used to infrastructure, tooling, and support systems that don't exist at a startup. Over-engineers everything. Doesn't understand startup pace.
The Pattern
The Big Tech Transplant has an amazing resume. Staff engineer at Google. Tech lead at Meta. Principal at Amazon. They've built systems at scale that handle millions of users.
The problem? At Big Tech, they had thousands of engineers, world-class infrastructure, and support systems for everything. Testing frameworks, CI/CD pipelines, monitoring, on-call rotations. All built by dedicated teams.
At a startup, none of that exists. And instead of adapting, the Big Tech Transplant tries to recreate what they had. They spend months on "foundational work" while competitors ship products.
Warning Signs
- Building "scalable infrastructure" for 10 users. Kubernetes clusters, microservices, the works. For a product that hasn't found PMF.
- Runway burning on tooling. Weeks spent on "proper CI/CD" before shipping a single feature.
- Enterprise patterns for a team of 3. Code reviews by committee. Design docs for everything. Process paralysis.
- "This is how we did it at Google." Constant references to Big Tech practices that don't apply at your scale.
- Underestimates everything. "This would take 2 days at Meta" but here there's no internal API, no library, no dedicated team.
- Frustrated by "chaos." Startups are messy. They expect order that doesn't exist yet.
Why This Happens
Big Tech engineers are genuinely skilled. But they've been trained in an environment with essentially unlimited resources. They've never had to ship with duct tape and prayers. They don't know how to cut scope ruthlessly.
Founders hire them because the resume is impressive and they want "enterprise quality." But at the seed stage, you don't need enterprise quality. You need to learn what to build.
The mismatch isn't about ability. It's about context. What makes you successful at Google actively hurts you at a 10-person startup.
How to Fix It
- Set aggressive shipping deadlines. Force the tradeoffs. "We ship in 2 weeks. What's the simplest version?"
- Ban premature infrastructure. No Kubernetes until you have 100 paying customers. No microservices until the monolith hurts.
- Pair them with startup veterans. Someone who's shipped scrappy can model the right behavior.
- Reframe success metrics. Not "code quality" but "learning velocity." Ship, measure, iterate.
- Have the calibration conversation. They may not realize how different this is. Some adapt quickly once they understand.
How I Can Help
I've been on both sides. I've worked at Meta, Instagram, and TikTok, and I've also been engineer #1 at scrappy startups. I know what matters at each stage and how to translate between the two worlds.
I can help recalibrate a Big Tech Transplant, set the right expectations, and build processes that are appropriate for your stage, not the stage they came from.
Recognize this pattern?
30-minute diagnostic call.
Tell me what's happening. I'll tell you the one thing to fix first.