Technical practice
Designing Modern Lakehouse Architectures
What to coverExplain the core components of a Lakehouse architecture and how it bridges the gap between data warehouses and data lakes. Focus on how technologies like Delta Lake provide ACID transactions, schema enforcement, and time travel to support both BI and AI workloads.
Scaling Distributed Data Systems
What to coverDiscuss the challenges of scaling distributed data systems in a cloud environment. Address how you manage data partitioning, shuffle optimization, and resource allocation in Apache Spark to ensure performance for large-scale enterprise datasets.
GenAI and ML Lifecycle Management
What to coverDescribe how you would architect a production-grade GenAI pipeline using MLflow. Emphasize the importance of model tracking, registry management, and deployment strategies for LLMs within a secure enterprise environment.
Data Governance with Unity Catalog
What to coverExplain the role of centralized governance in a multi-cloud or hybrid enterprise environment. Discuss how you would implement fine-grained access control, data lineage, and auditing to ensure compliance and security across diverse data assets.
Cloud-Native Integration Strategies
What to coverDetail your approach to integrating Databricks with native cloud services (AWS, Azure, or GCP). Focus on trade-offs between using cloud-native storage versus platform-specific features, and how to optimize for cost and latency.
Proof of Concept (PoC) Execution
What to coverOutline your methodology for running a successful PoC for a Fortune 500 client. Suggestion: Focus on defining clear success criteria, identifying technical blockers early, and aligning the PoC outcomes with the customer's specific business KPIs.
Optimizing SQL and Python Workloads
What to coverCompare the performance implications of using SQL versus Python/Scala for large-scale data transformations. Discuss when to leverage Spark SQL for optimization versus when custom code is necessary for complex logic.
Build vs. Buy Decision Frameworks
What to coverHow do you guide enterprise customers through build-versus-buy decisions for AI infrastructure? Suggestion: Address factors such as total cost of ownership, time-to-market, maintenance overhead, and the ability to customize or scale the solution.
Behavioural practice
Managing Strategic Stakeholder Relationships
What to coverDescribe a time you built a durable relationship with a senior technical leader at a large enterprise. Focus on how you earned their trust, influenced their architectural decisions, and maintained alignment over a long-term engagement.
Navigating Complex Technical Disagreements
What to coverProvide an example of a situation where you disagreed with a customer's proposed technical approach. Explain how you used data and architectural best practices to persuade them toward a more scalable or efficient solution.
Collaborating Across Global Teams
What to coverShare an experience where you had to coordinate with internal product, engineering, or sales teams across different geographies. Highlight how you ensured consistent architectural guidance and communication despite time zone or cultural differences.
Driving Adoption in Competitive Landscapes
What to coverDiscuss a scenario where you had to position a platform against a strong incumbent or competitor. Explain how you identified the customer's unique pain points and demonstrated the specific value proposition of your solution to win the account.