From Fresher to Data Engineer: Sai Sravan’s Career Growth Story

Discover Sai Sravan’s journey from a fresher to a Data Engineer, highlighting his training, hands-on projects, mentorship, and steps toward career growth.

Prerana B A Prerana B A
Sep 10, 2026 Updated: Sep 10, 2026
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From Fresher to Data Engineer: Sai Sravan’s Career Growth Story
DataMites Data Science Success Story by Sai Sravan

Starting a career in the data field as a fresher often involves exploring different technologies, understanding where your interests lie, and building the right skills. Sai Sravan Malyala, a 2025 B.Tech graduate in Computer Science and Engineering, began his career journey with an interest in full-stack development. After graduation, his growing interest in data-related technologies led him to explore a new career direction.

Sai started with Data Analytics to build his foundation and gradually developed his technical and practical skills through training, projects, and interview preparation. His efforts eventually helped him secure a Data Engineer role at Confideo IT Services through his first successful client interview. This DataMites Success Story reflects how continuous learning, practical experience, and the willingness to explore new opportunities can help freshers move toward their career goals.

From Fresher to Data Engineer: Sai Sravan’s Success Story

Sai Sravan Malyala’s journey shows how a fresher can build a career in the data field through structured learning, practical projects, and consistent preparation. Starting with Data Analytics, he developed his technical foundation and eventually secured a Data Engineer role at Confideo IT Services.

1. Can you tell us about yourself?

My name is Sai Sravan Malyala. I am a recent B.Tech graduate in Computer Science and Engineering, and I graduated in 2025. Initially, I was interested in full-stack development. After graduation, I realized that I was more interested in data-related technologies.

2. What made you explore a career in data?

After completing my graduation, I explored different data-related technologies and career options, including Data Analytics, Data Science, and Data Engineering. I wanted to understand the fundamentals first, so I decided to start with Data Analytics.

3. Why did you choose to start with Data Analytics?

I wanted to start with the basics and build a strong foundation before exploring other areas of the data field. Data Analytics gave me an opportunity to understand important concepts and technologies step by step.

4. How did you come to know about DataMites?

I already knew about DataMites, but one of my friends suggested the institute. Three of us became interested in joining, and we joined together. That is how my learning journey started.

5. What did you learn during your Data Analyst course?

I learned Python, MySQL, Power BI, EDA, ETL, ELT, and other concepts related to Data Analytics. These technologies helped me develop a better understanding of working with and analysing data.

6. Did you face any difficulties while learning these technologies?

Yes. Most of the tools were new to me, so initially I found some concepts difficult to understand. However, the trainers were friendly and approachable, and I could ask questions and clarify my doubts whenever needed.

7. How important were projects in your learning experience?

Projects were an important part of my learning. I completed three projects, including two capstone projects and one client project. The projects helped me apply the concepts I learned during training in practical situations.

8. How did the trainers and doubt-clearing sessions help you?

Whenever I had doubts, I could approach the trainers and attend doubt-clearing sessions. Their guidance helped me understand difficult concepts better and improved my confidence during the learning process.

9. How did the PAT team support you during your journey?

The PAT team supported me whenever I had issues related to the course or certifications. They also helped me with the interview preparation process and provided feedback whenever I needed improvement.

10. Did you clear your mock interview on the first attempt?

No. I did not clear my first mock interview. The PAT team explained the mistakes I had made and helped me understand the areas I needed to improve. I worked on those areas and cleared the mock interview on my second attempt.

11. What was the structure of your Data Engineer interview?

There were two interview rounds. The first round included self-introduction and basic questions related to Python, SQL, Power BI, and other technologies I had learned. The second round focused more on technical questions.

12. What technical questions were asked during the second interview?

I was asked a Python coding question and an SQL query based on a given situation. There were also intermediate-to-advanced technical questions. During the interview, I had to write the coding solutions on paper.

13. How did you feel about getting a Data Engineer role after training in Data Analytics?

I was happy with the opportunity. I did not feel that my current role had to exactly match the domain of my previous course. I was interested in learning new things and exploring new technologies through the Data Engineer role.

14. How did the LMS and regular practice help you prepare?

The LMS allowed me to revisit and rewatch training videos whenever I needed revision. Initially, I did not practice regularly, but my trainer advised me to practise every day. Practising a topic on the same day helped me understand and remember it better.

15. What advice would you give to freshers preparing for a career in data?

My advice is to practice every day. If you learn a topic today, practice it on the same day. This makes it easier to remember the concepts and prevents too many topics from accumulating for later revision. I would also suggest staying open to learning new technologies and using practical projects to strengthen your skills.

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Key Takeaways from Sai Sravan’s Data Engineer Career Journey

Sai Sravan’s journey shows how focused learning, practical experience, and consistent preparation can help freshers build careers in the data field. His transition from Data Analytics training to a Data Engineer role highlights the value of adaptability and continuous learning.

  • Career Direction: Sai shifted his career interest from full-stack development to data-related technologies after graduation.
  • Strong Foundation: He started with Data Analytics to understand the fundamentals before exploring advanced data roles.
  • Technical Skills: His training covered Python, MySQL, Power BI, EDA, ETL, and ELT, building a foundation in data technologies.
  • Practical Experience: Completing two capstone projects and one client project helped him apply his learning to practical situations.
  • Trainer Support: Friendly trainers and doubt-clearing sessions helped him overcome challenges and understand difficult concepts.
  • Interview Preparation: Feedback from his first mock interview helped him identify mistakes, improve his preparation, and clear the second attempt.
  • Career Opportunity: His first successful client interview led to a Data Engineer role at Confideo IT Services.
  • Continuous Learning: Sai continued learning beyond the regular curriculum, exploring concepts such as OLAP and OLTP.
  • Daily Practice: He found that practising topics on the same day helped him retain concepts and prepare more effectively.
  • Adaptability Matters: His journey demonstrates the importance of being open to new roles, learning new technologies, and continuously improving skills.

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Sai Sravan Malyala’s journey from a fresh B.Tech graduate to a Data Engineer at Confideo IT Services shows how practical learning, consistent preparation, and a strong foundation can support career growth. His Data Analytics training, projects, trainer guidance, and mock interviews helped build his technical skills and confidence. His willingness to learn new technologies also shows the value of staying open to opportunities. For freshers, his journey highlights how choosing a Top IT Courses and practising consistently can help build a strong foundation for a career in technology.

Sai Sravan’s transition from a fresh graduate to a Data Engineer at Confideo IT Services was supported by structured learning and practical experience. According to Research and Markets, the global Big Data Engineering Services Market is expected to grow from USD 91.54 billion in 2025 to USD 213.07 billion by 2031, at a 15.12% CAGR. Through projects, trainer guidance, mock interviews, and regular practice, Sai strengthened his skills and confidence. His journey shows how choosing the right Data Science institute in Hyderabad and staying open to new technologies can help freshers explore opportunities in the data industry.

Whether you’re a fresher or an aspiring data professional like Sai Sravan, DataMites Institute offers structured learning to help develop practical, industry-relevant skills. With 50+ offline centres and online learning options, DataMites provides Data Science courses in Delhi and training opportunities in Chennai, Hyderabad, Pune, Mumbai, Coimbatore, Ahmedabad, Kolkata, Noida, Indore, Jaipur, Chandigarh, Kochi, Nagpur, and Bhubaneswar. DataMites has also received the Best Skill Development EdTech award, recognising the company’s contribution to skill development and career-focused learning. Through hands-on projects, expert mentorship, internship opportunities, interview preparation, and IABAC® and NASSCOM FutureSkills® certifications, learners can prepare for careers in the data industry. Sai Sravan’s success story demonstrates how consistent learning, practical experience, and proper guidance can help freshers pursue opportunities in Data Science and Data Engineering.