Sathe moved from India to the US in 2019 to study information systems. He began searching about 18 months before graduation, focusing on data engineering and privacy through relevant classes, internships and side projects. In his second semester, he started reviewing job postings to see what employers wanted.
For advice, he messaged active LinkedIn users in data engineering at Big Tech companies, choosing engineers and senior engineers rather than executives. His note explained what he was studying, his career goal and his technical skills, then asked what to improve. Many people did not respond, he said, but after finding three or four good mentors, he sent fewer messages.
One contact pointed out that Sathe’s résumé made him look inexperienced. Although he had three years of work experience in India after his bachelor’s degree, he had placed his education ahead of that experience. He said he began getting more interviews after changing the résumé. He now replies to almost all LinkedIn messages seeking career advice.
He also split applications into two groups: companies he especially wanted to join and a wider set of roles for which he applied in greater numbers. He checked target companies’ career pages a couple of times a week and tailored those applications, while using a generic résumé for volume applications. After receiving an offer for a data engineering job at Walmart, he stopped applying broadly and focused on roles that could match or exceed it.
Looking back, Sathe says applicants today should build portfolio projects that work as live products with real users, and develop fluency in AI tools and how they work. Coding and code structure remain important, he said, as do system design and architecture, areas where human judgment still matters.
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