The interview process consisted of five structured rounds, each designed to evaluate a different dimension of my skills and fit: 1. Hiring Manager Round: This was a conversational discussion focused on my current role, project scope, and day-to-day responsibilities. The interviewer explored my experience with the data stack and how I contribute to the broader business context. 2. Technical Assignment (SQL + PySpark): I was given a take-home assignment involving real-world SQL and PySpark problems. The focus was on writing clean, optimized code for data transformation and aggregation tasks, while also handling edge cases effectively. 3. Technical Deep-Dive (Live Interview): This was a detailed technical round covering my resume projects and knowledge areas like Virtual Networks (VNet), CI/CD practices, Git workflows, ML model deployment basics, and other cloud engineering topics. The interviewer assessed both depth and breadth. 4. Project Presentation Round: I was asked to present one of my past projects end-to-end. This included discussing the architecture, tools used (ADF, ADLS, Databricks, Snowflake), problems encountered, solutions implemented, key learnings, and my personal technology preferences. The panel evaluated my ability to communicate technical decisions clearly. 5. Executive Fit/Client Communication Round: The final round focused on stakeholder management and soft skills. I was asked behavioral questions about handling difficult client asks, ambiguity, ownership, and real-life client conversations. The emphasis was on how I communicate complex data topics to non-technical stakeholders and collaborate under pressure.