From Confused Engineering Grad to Data Analyst: Sriyanka's Story

At a glance

• Before: Fresh engineering graduate with some SQL and Python from college, unsure which career to pick
• Now: Data analyst at a startup, turning data into business insights
• Program: NextLeap Data Analyst Fellowship
• What made the difference: Projects, case studies, and AI and mentor mock interviews that fixed how she communicated
• Biggest lesson: "Every rejection taught me something new."

Sriyanka finished engineering the way many graduates do: with a degree, some coding skills and no clear idea of what came next. She knew SQL and Python from college, but her first interviews showed her that tools alone weren't getting her hired. If you're wondering how to become a data analyst after engineering, her story shows the step most people miss: learning to solve business problems and explain your thinking out loud. Today she works as a data analyst at a startup. She sums up the journey in three words: "exploration, consistency and persistence."

Where did Sriyanka start?

Sriyanka started as a recent engineering graduate exploring her options and trying to work out which role would suit her best.

"After graduation, I was exploring different career options, and I was also trying to understand what would be a better fit for me," she says.

A friend recommended NextLeap. She first looked at the Product Manager Fellowship, but while browsing the programs she found the newly launched Data Analyst Fellowship. She had already worked with SQL and Python during her degree, so data analytics felt like a more natural fit. She joined the fellowship in February 2025.

Is knowing SQL and Python enough to become a data analyst?

No. Sriyanka learned in interviews that companies want analysts who can solve real business problems and explain their reasoning, not just people who know the tools.

"During engineering I had learned SQL and Python, so I was comfortable with the basics," she says. "But when I started giving interviews, I understood that companies were not just looking for someone who knew the tools. They wanted someone who could solve real business problems and explain their thought process."

That changed how she prepared. Instead of collecting more tools, she focused on projects, case studies, communication and interview practice. This was also why she chose a structured program. "It offered a more structured path to becoming job-ready," she explains, "not just learning SQL, Python, Excel and Tableau."

How did Sriyanka prepare for data analyst interviews?

She practised with AI mock interviews and mentor mock interviews during the NextLeap Data Analyst Fellowship until explaining her approach became second nature.

Her biggest challenge was not technical. "It wasn't that I didn't know the answer," she says. "Many times I knew how to solve the problem, but I struggled to explain the thought process." She admits she would sometimes "jump directly to the answer without explaining my approach," and at other times "get nervous and lose my train of thought."

The mock interviews gave her a safe place to fail and get feedback. "What stood out to me was not just the technical learning but also the interview preparation," she says. "The AI mock interviews and the mentor mock interviews helped me to improve my communication and also the confidence, and [the] ability to present my project in the interview."

Over time she learned to structure her answers and explain her reasoning step by step. One habit changed more than any other:

"Earlier I used to think that interviews were all about giving the right answers. Later I realised the interviewers want to understand your thinking process."

"Now, instead of directly giving the answers, I explain how I'm approaching the problem, what factors I'm considering, and then I give my conclusion," she says. "That small change made a huge difference in my interviews."

The rejections didn't stop overnight. "Even after all the preparation, I faced multiple rejections," she says. "But I think every rejection taught me something new and also helped me to improve." She eventually got an internship at a startup called Mellow Carbon, where she now applies what she learned to real business problems.

What does a data analyst actually do?

A data analyst turns raw data into insights that help a business make decisions. That means analysing data, spotting trends, building reports and answering business questions.

"As a data analyst, my role is to turn data into insights that could help the business make better decisions," Sriyanka says. Because she works at a startup, she also sees many parts of the business. She says this has shown her how analytics creates business impact "beyond the dashboards and reports."

The biggest surprise was how much of the job is about defining the problem in the first place:

"In a project, you are usually given a problem, but in a company you often have to identify the problem yourself. That's when I realised that analytics is not just SQL, Python or dashboards. It's also helping the business make better decisions."

How does Sriyanka use AI as a data analyst?

She mainly uses ChatGPT for research, understanding concepts and troubleshooting queries, and she always checks what it gives her.

"I use ChatGPT to understand the research and the concepts, and sometimes to troubleshoot queries and approaches for the analysis," she says. She is careful not to lean on it too much, because it "sometimes gives you wrong answers." Her rule: "I still verify the outputs, because business decisions require human judgment, not AI."

Sriyanka's roadmap for aspiring data analysts

Her first piece of advice is simple: don't panic. "It's completely normal to feel confused, especially after graduation. Even I was confused," she says. What helped her was breaking the journey into smaller steps instead of trying to plan her whole career at once:

1. Understand the role. Find out what a data analyst actually does. Talk to people in the field, watch videos and do your own research.
2. Learn the fundamentals. Start with SQL, Excel and Data Visualisation.
3. Build projects and case studies. "That's where the real learning happens."
4. Polish your resume and portfolio. Put your projects and case studies up front.
5. Work on communication and interview practice. Practise explaining your approach before your answer.
6. Stay consistent. "You don't have to learn everything in one month. Small improvements every day add up over time."

Watch the full conversation

[YouTube embed: https://www.youtube.com/watch?v=M_wQEvzCA0c]

Sriyanka spoke with Nimisha on The NextLeap Podcast.

Start your own switch into data analytics

Like Sriyanka, many learners already know some SQL or Python. What they're missing is the practice of solving business problems and explaining their thinking under interview pressure. The NextLeap Data Analyst Fellowship combines hands-on projects and case studies with AI and mentor mock interviews, so you're ready for the job and not just the syllabus. Explore the Data Analyst Fellowship and see if it's the right next step for you.

Frequently asked questions

Can you become a data analyst right after engineering? Yes. Many engineering graduates move into data analytics because they already have some exposure to SQL, Python and logical problem-solving. The gap is usually job-readiness: working on real business problems, building a portfolio of projects and case studies, and explaining your reasoning clearly in interviews. Sriyanka joined a structured fellowship after graduation and landed a data analyst role.

Is knowing SQL and Python enough to get a data analyst job? Usually not on its own. Employers expect analysts to use tools like SQL, Python, Excel and Tableau to solve real business problems and to explain their thought process and insights clearly. Projects, case studies and communication practice help close the gap between knowing the tools and being job-ready.

How do I prepare for a data analyst interview? Practise explaining your approach before giving an answer: say how you're framing the problem and which factors you're weighing, then give your conclusion. Mock interviews with AI tools or mentors give you a safe place to rehearse and get feedback. Be ready to walk through your projects and the business impact of your analysis.

What does a data analyst do? A data analyst helps turn data into insights for business decisions. Typical work includes analysing datasets, identifying trends, creating reports and dashboards, and answering specific business questions with data. At a startup,data analysts often get exposure to several business functions.

Can AI replace data analysts? AI tools like ChatGPT can speed up research, help explain concepts and help troubleshoot queries, but their output needs checking. Business decisions still depend on human judgment, context and a clear understanding of the problem, which remain core parts of a data analyst's job.