Azure/Cloud: How would you design a multi-region, high-availability architecture for a data pipeline in Azure? Explain the concept of Azure Synapse Analytics and how you would use it in big data scenarios. What are some best practices for managing and securing sensitive data in cloud environments, especially for financial clients? PySpark/Python: How do you optimize the performance of Spark jobs in a production environment with large datasets? Provide examples. How would you design a scalable data pipeline that processes streaming data in real-time using PySpark and Kafka? Write a Python script that connects to an API, fetches the data, and performs transformation on large datasets before loading it into a data warehouse. SQL/Database Design: How would you design a database schema for a large-scale e-commerce platform? What normalization techniques would you use? What is your approach to indexing in SQL for high-performance querying in large datasets? Can you explain the difference between clustered and non-clustered indexes? Write an optimized SQL query to calculate the running total of a column over a specific time window, ensuring performance on large datasets. Big Data and Scalability: What is the MapReduce framework? Can you explain how it works with an example of a data transformation pipeline? How would you ensure that a data pipeline can handle petabytes of data while maintaining reliability and performance? Explain the architecture and process flow of a Lambda architecture in big data systems. General Problem Solving: Given a data pipeline that is consistently failing at a certain point, how would you systematically troubleshoot and resolve the issue? Explain a time when you had to design a solution for a complex data integration problem with multiple disparate data sources. Behavioral and Situational Questions: Tell us about a time when you had to challenge the status quo to improve a process or project. How did you approach it, and what was the outcome? Describe a time when you had to work on a project that had conflicting priorities or requirements. How did you manage the situation? How do you handle failure in high-stakes situations, especially when it affects a project or client? Can you give an example? As a data engineer, how do you ensure the scalability and reliability of data pipelines in a fast-growing business? Can you describe a situation where you had to work with cross-functional teams (like product, marketing, or operations)? How did you communicate technical challenges to non-technical stakeholders?