I landed 4 job offers out of grad school and picked Meta. These 2 tactics helped me land my ideal offer.

This as-told-to composition is according to a conversation alongside Adwait Sathe, a 32-year-old elder data privacy engineer at Meta Superintelligence Labs. It has been edited for dimension and clarity.

I've been operating at Meta for the final five years, archetypal at Messenger, and for the final three years as a data privacy engineer at Meta Superintelligence Labs.

I moved to the US from India in 2019 to pursue a master's degree in data systems at Northeastern University in Boston, graduating in 2021.

I knew the job use procedure out of university would be competitive, so I started my job hunt concerning a twelvemonth and a fractional before I graduated.

I was extremely apparent from the start: I desire to activity in data engineering privacy. I took classes in that site and built a applicable portfolio through internships and side projects.

In my second semester, I started looking at job postings I would use for a twelvemonth afterward to comprehend what the industry was looking for.

I had four offers in hand whenever I graduated — from McKinsey, Walmart, Amazon, and Meta. Here's the scheme I used to method my job search.

Cold messaging group who weren't too senior

I had a maneuver for cold messaging people on LinkedIn.

I connected alongside a lot of group on LinkedIn who were operating on things akin to what I wanted to activity on.

The group I reached out to were energetic on LinkedIn and operating in Big Tech in data engineering roles, not managerial roles. Engineers and elder engineers, not vice presidents or directors.

I was looking for mentorship. I approached them to comprehend the industry's requirements, so that whenever I applied, my use would remain out among the masses.

My chilly communication was extremely simple: "Hey, I am Adwait, I'm studying this, and I desire to prosecute my occupation in this. I have this tech stack and résumé; what should I improve?"

One association gave me outstanding direction — that my résumé made it appearance akin I wasn't experienced.

I had three years of activity cognition in India following getting my bachelor's degree. Still, whenever I archetypal put together my résumé, it looked akin a new grad résumé since I front-loaded my learning complete my activity experience.

I started getting additional interviews whenever I fixed that issue.

When I started chilly messaging alongside this strategy, I aimed for volume, sending messages to a lot of people. Many didn't reply.

But formerly I had gotten three or four fine mentors, I eased up on messaging, knowing that these group were really firm and would assistance me out.

I've tried to continue it on. Now, I answer to nearly all LinkedIn messages I obtain that ask for occupation advice.

Splitting job apps by quantity and strategy

My second job hunt strategy was to divided my use procedure into two buckets.

The archetypal bucket: companies I really wanted to activity for.

I had a catalog of companies I wanted to activity for, and I used to go to their occupation websites a brace of times a week to inspect for new positions and use early. I would be looking out for new grad positions, which are typically released way before graduation season.

The second bucket was wider. This was all concerning volume, applying to as many roles as possible.

I had a generic résumé for all the volume-based applications. But for the ones I really wanted, I tailored all application.

After I got my archetypal offer, a data engineering function at Walmart, I stopped applying in volume. The scheme following that was to use for roles I really wanted, which could equivalent or exceed what I'd already gotten.

How I would use alternatively today alongside AI

If I were applying for the identical roles today, I would create certain to have a lot of distinct projects in my portfolio, since construction has rotate into so important.

A operating product, which is live and has genuine users, is way improved than bringing a prototype into the discussion and saying, "I built this prototype, it worked whenever I built it, but it's not operating now."

The second item is AI fluency. This doesn't fair average understanding distinct tools, but additionally how AI plant in the backdrop and a judgement of whenever to use which tools.

But the fundamentals don't change. Being fine at coding and knowing how to structure the code is motionless important. AI can grip implementation, but designing systems and architecture is motionless extremely crucial and human.