Sai Pranav’s Interview Query Success Story: From Data Analyst to BI Engineer at Amazon

After navigating a competitive job market and a demanding interview process, Sai Pranav landed a Business Intelligence Engineer role at Amazon, where he now works on the supply chain team. Before joining Amazon, Sai had already built experience as a data analyst at a startup in India and later pursued his master’s degree in the US.

Despite his background, breaking into a top tech company was not an easy task. The biggest challenge was mastering interviews where “there is no ground for error,” especially in SQL-heavy rounds. Sai needed a way to practice deeply, move faster, and understand exactly what interviewers expected. Through months of disciplined preparation and consistent problem-solving, he turned that challenge into a strength and ultimately landed a job at Amazon, marking a clear data science interview success and Interview Query success story.

Sai’s career in data began in India, where he worked as a data analyst at a small startup. That role gave him hands-on exposure to real datasets and day-to-day analytical problems. Wanting to deepen his skills and expand his opportunities, he decided to pursue a master’s degree in the US.

After graduating, Sai continued working in data roles at startups before setting his sights on larger companies. Along the way, he developed a practical view of interview prep. As he put it, “There’s no secret sauce behind it. I just solved as many problems as I could.” That belief shaped how he approached interviews, focusing less on shortcuts and more on fundamentals and repetition.

Sai’s job search unfolded in a tough market with fewer openings and intense competition. Even with experience, landing interviews required persistence. Once the interviews started, the pressure increased.

For data roles, Sai quickly realized that SQL was unavoidable. “SQL is something non-negotiable for any role,” he explained. Interviews tested not only correctness but also speed and clarity. On top of that, technical and behavioral questions were often mixed together. “There is no specific separation. Everything is mixed,” Sai said, describing how interviewers evaluated both problem-solving and communication at the same time.

Sai discovered Interview Query after hearing it described as “like LeetCode for data science roles.” He started with a one-month trial, quickly saw its value, and upgraded to a full-year subscription.

Rather than focusing on company-tagged questions, Sai chose a broader approach. “I didn’t use company tags at all. I just solved medium problems, hard problems, as many as I could,” he shared. He worked through SQL, Python, analytics, and data science case studies, using timers to simulate interview conditions.

One feature that stood out was exposure to multiple solutions. “I used to learn from other people’s solutions, not just my solution,” Sai said. That helped him understand alternative approaches and recognize patterns faster. Over time, SQL and Python practice on Interview Query played what he called “a very big role in my learning curve.”

Amazon’s interview process was intense and varied. SQL formed the backbone of almost every round. “They can ask anything under the sun regarding SQL,” Sai recalled. Questions focused on window functions, ranking, aggregation, and removing duplicates, all tied closely to real business scenarios.

Python questions ranged from medium to hard difficulty, while case studies tested analytical thinking and product sense. Sai encountered prompts similar to what he practiced on Interview Query, such as diagnosing metric drops and reasoning through A/B testing scenarios. Because he had already solved many comparable problems, the interview questions felt familiar rather than intimidating.

After completing multiple rounds, Sai received an offer from Amazon for a Business Intelligence Engineer role on the supply chain team. Reflecting on the outcome, he stayed grounded. “I just solved the problems, and I got lucky,” he said. “It’s just hard work and some luck.”

Now nearly eight months into the role, Sai describes Amazon as “a very great place to work. It’s challenging, you can learn a lot, but it’s also fun.” The offer validated months of focused preparation and confirmed that consistent effort pays off, even in a difficult hiring market.

Based on his experience, Sai offers practical advice for others preparing for data interviews:

• Treat SQL as non-negotiable and aim to be fast and accurate
• Solve a high volume of medium and hard problems
• Practice under time pressure to build speed
• Learn from multiple solutions, not just your own
• Focus on fundamentals instead of guessing interview questions

For candidates starting, Sai believes Interview Query is “a better starting point if you don’t know anything about data science roles.”

Sai Pranav’s journey from a startup data analyst to a BI Engineer at Amazon highlights the power of consistent practice and realistic preparation. His story reinforces a simple truth. There is no shortcut. Progress comes from repetition, pattern recognition, and steady effort.

Interview Query supported that journey by exposing him to realistic SQL, Python, and case study questions that closely matched real interviews. For readers preparing for their own data science interview success, Sai’s experience shows that with the right preparation strategy and persistence, landing a job at Amazon is achievable. Explore Interview Query’s interview guides, question library, and other success stories to start building your own path.