Startup-to-FAANG Resume Translator

At your startup, you basically are the data function. Your resume doesn't say that. It says small things, because your startup never tracked the numbers Big Tech resumes expect.

You've owned pipelines end to end, made architecture calls nobody double-checked, touched more of the stack than most Big Tech DEs your level ever will. Then you apply to Google, Amazon, or a FAANG GCC, and you get screened out before anyone hears your actual story. This isn't a resume-writing problem you can fix with better adjectives. It's a translation problem. This guide fixes it, without inflating a single line.

What's inside: The Metric Reconstruction Method (working backward to credible numbers your startup never tracked), the Breadth Reframing Guide (structuring "I did everything" into parseable ownership areas), the Comp Multiplier Evidence Builder (four categories of evidence for your target comp jump), and the Credibility Line (exactly where reconstruction stops being honest and starts being inflation).

Who this is for: Data Engineer at a Series A-C Indian startup, 3-7 YOE, targeting Big Tech or a FAANG GCC, resume feels thin next to Big Tech peers.

Who it's not for: you're at a services company (that's the Services-to-Product Transition Kit), or you're comfortable inflating numbers (this guide draws a hard line against that).