Nineleaps Technology Solutions Data Analyst Interview Questions
& Process

It was a pleasant and fast-paced interview process, well-organized and designed to make candidates feel comfortable. The panel ensured clear communication, especially if I was feeling nervous. The overall communication and process were smooth and seamless, making it a great opportunity for anyone starting in this field. The company also offers a dynamic growth period.

So, the interview process had a standard initial aptitude round which was fair. Then there was a Group Discussion on the topic 'Future of Electric Mobility' which was pretty relevant. But the cons were, like, the interviewer was so unprofessional, totally disengaged during the GD and even the personal interview, which made the whole atmosphere feel hostile and dismissive. They asked literally zero technical questions about SQL, Python, or Power BI, even though I applied for a technical role. Instead, they grilled me about my family background, college admission details like CET vs. Management quota, and my rank, and they seemed to look down on me after hearing it, showing clear prejudice. The interview ended abruptly with a sudden aptitude question instead of a technical one. When I stumbled, they just said, 'That's it, you may leave,' and outside another person rudely told me to 'Go home.' It was super humiliating and felt more like an interrogation than an interview. Management should really train interviewers to focus on skills, not college admission history, and basic respect is a must.

The hiring process was thorough, with four stages to assess everything from basic skills to complex technical problem-solving. First, an online assessment of moderate difficulty covering quantitative aptitude, logical reasoning, and verbal ability. Second, a quick group discussion (GD) with 7 members, where we had just 1 minute to prepare for a topic and a total of 10 minutes for the discussion, emphasizing the need for concise and impactful contributions. Third, a demanding technical interview focusing on advanced SQL and database management (specifically advanced SELECT, aggregations, and joins for filtering large datasets and calculating metrics), product sense and business analytics (conceptualizing analytics dashboard architecture, data modeling, and tracking user engagement vs. content creation), the Python data stack (practical Pandas and NumPy skills for data manipulation), and logical reasoning/problem-solving (brain teasers and comparative case studies). Lastly, a final HR interview to assess behavioral aspects.