From Business Insider · Aditi Bharade · · 2m
How I landed 4 offers and chose Meta
Adwait Sathe shares the two strategies that helped him secure his ideal job offer after grad school.
In brief
Adwait Sathe shares the two strategies that helped him secure his ideal job offer after grad school. A Meta data privacy engineer reveals how cold messaging mentors and splitting applications into targeted and volume buckets helped him land four job offers and choose his ideal role. Originally reported by Business…
Four offers after graduation
Adwait Sathe, now a senior data privacy engineer at Meta Superintelligence Labs, received job offers from McKinsey, Walmart, Amazon, and Meta after graduating in two thousand twenty-one.
Starting early with clear focus
Sathe began his job search a year and a half before graduation. He took classes in data engineering privacy and built a relevant portfolio through internships and side projects.
Cold messaging engineers, not executives
Sathe connected with active LinkedIn users in Big Tech data engineering roles. He targeted engineers and senior engineers, not vice presidents or directors, seeking mentorship and industry insight.
“Hey, I am Adwait, I'm studying this, and I want to pursue my career in this. I have this tech stack and résumé; what should I improve?”
Fixing his résumé with advice
One mentor told Sathe his résumé looked like a new grad's despite three years of work experience in India. He front-loaded education over work experience. Fixing this issue led to more interviews.
Two-bucket application strategy
Sathe split applications into two buckets. First, companies he really wanted to work for, checking career websites twice weekly for new grad positions. Second, volume applications with a generic résumé.
Tailoring applications for top choices
For companies he really wanted, Sathe tailored each application. After receiving his first offer, a data engineering role at Walmart, he stopped volume applications and focused only on roles matching or exceeding that offer.
Building live products matters most
If applying today, Sathe would prioritise a portfolio with different projects. A working product with real users beats a prototype that no longer works when brought to an interview.
“A working product, which is live and has real users, is way better than bringing a prototype into the interview and saying, "I built this prototype, it worked when I built it, but it's not working now."”
AI fluency and fundamentals endure
Sathe emphasises AI fluency means understanding how AI works and when to use which tools. But fundamentals remain crucial: coding ability and system architecture design are still human skills AI cannot replace.
“AI can handle implementation, but designing systems and architecture is still very important and human.”
The main idea
A Meta data privacy engineer reveals how cold messaging mentors and splitting applications into targeted and volume buckets helped him land four job offers and choose his ideal role.
