“Relevant industrial skills” — this was the phrase data engineer Sunjana Ramana repeated during our call. If I had to count, she probably said it over 11 times. It may not sound like much, but it quickly became a recurring theme.
And for good reason. When it comes to landing jobs in data engineering, especially as a fresh graduate, having “relevant industrial skills” makes all the difference.
“I applied to over 1,200 jobs throughout my master’s programme, but honestly, it could have been more than a couple thousand, and I still struggled to secure a job,” Ramana recalls.
She’s far from alone. Several students have shared with Study International how it took them up to thousands of applications and years to land a role.
“It’s incredibly difficult to get into data engineering in the US, even with an Ivy League degree,” Ramana says. “It’s even harder if you’re not from the field, like me.”
Ramana is a full-time data engineer, AWS Activate Fellow at Amazon Web Services, Founders Hub Member at Microsoft for Startups, and a Founder at DataDrooler Community. Source: Sunjana Ramana
How to get jobs in data engineering, even if you don’t have a degree in it
Finding jobs in data engineering wasn’t Ramana’s original plan. She had earlier earned a dual undergraduate degree in electrical engineering and marketing from Jawaharlal Nehru Technological University, Hyderabad.
It wasn’t until she began researching master’s programmes that she made the switch.
“After finishing my bachelor’s, I realised it wasn’t the direction I wanted to pursue,” she says. “I was drawn to renewable energy or data science — so I decided to build skills in the latter.”
She joined Columbia University’s Master’s in Electrical Engineering, specialising in data-driven analysis and computation.
But once classes began, she quickly realised she was behind.
Ramana alongside her family at her graduation from Columbia University. Source: Sunjana Ramana
“Everyone else was already applying for internships or working in data engineering from the start. I had no experience,” she says.
During her first semester, she began applying for data engineering internships, but the results were dismal.
“I faced rejection after rejection. I applied to hundreds, but only 10 replied, and I still didn’t land anything,” she continues.
She ramped up her applications and tried networking within the field, but that alone wasn’t enough.
While many assume that attending a top-tier school guarantees easy access to jobs, Ramana’s experience proves otherwise.
So what was holding her back? Simply put, no relevant industrial skills.
The solution? Gain experience through data engineering projects
Grades and prestige matter, but when it comes to jobs in data engineering, hands-on experience carries more weight.
“I thought acing interviews and submitting applications would be enough. It wasn’t,” she admits.
Ramana shifted focus. She selected project-heavy courses that offered real-world application rather than theory-based learning.
“I had the option between theoretical and project-based courses. I chose the latter to gain hands-on experience,” she explains.
She participated in data engineering projects across multiple sectors, gaining valuable insights into how the role operates across various industries.
That strategy paid off. Ramana landed a data and integration internship at QBE Insurance. She’s now a software engineer there.
Ramana is a x2 founder with a passion for helping job seekers and early career data professionals break into the field. Source: Sunjana Ramana
From job seeker to founder: Building tools and a community for data engineers
Ramana’s rough job search shaped her view of the US job market. One particular pain point was tracking job applications.
After graduating, she decided to create a tool for job seekers to monitor their application progress.
In August 2023, she launched JotterWolf, an AI-powered job tracker designed to collect, organise, and manage applications. It gained traction, featured by Microsoft for Startups, Columbia Startup Lab, and Wiggin & Dana Fellowship.
But JotterWolf only lasted seven months.
“I launched it during the first year of my OPT visa, but had to shut it down,” Ramana says.
Still, her time at Columbia revealed another gap: there wasn’t a strong community for data engineering students and professionals to connect and collaborate.
In her free time, Ramana enjoys gardening, painting, and baking. Source: Sunjana Ramana
“It was hard to find a space where data engineers could work together on projects or just share ideas,” she says.
That sparked her next venture. In September 2024, Ramana founded DataDroller Community, a collaborative, community-led platform for data engineers.
The goal was to help early-career professionals, such as Data Engineers, Data Scientists, and ML Engineers, grow through meaningful data engineering projects.
DataDroller provides a platform for experimenting with new ideas, building portfolios, and solving real-world problems, whether contributing to a team or launching a personal initiative.
“I started the DataDroller Community on Slack, hosting weekly meetups for brainstorming and collaboration,” Ramana says.
“Seeing so many data professionals connect was heartwarming, so I decided to scale it up.”
