Technical practice
Designing Scalable Data Architectures for GenAI
What to coverDiscuss how you would design a data architecture to support Generative AI applications. Focus on the trade-offs between batch and real-time processing, data lineage, and how to ensure the infrastructure remains scalable and secure as model requirements evolve.
Optimizing ETL Pipelines on AWS
What to coverExplain your approach to identifying and resolving performance bottlenecks in large-scale ETL pipelines using AWS services like Glue or EMR. Mention specific strategies such as partitioning, file format optimization, and resource allocation to improve throughput and reduce latency.
Implementing Agentic AI Patterns
What to coverDescribe your experience with agentic AI patterns such as tool use, function calling, or multi-agent orchestration. Explain how you have integrated these patterns into data engineering workflows to automate tasks or improve data accessibility.
Data Modeling for Diverse Storage Systems
What to coverCompare and contrast data modeling strategies for relational databases like Redshift versus non-relational stores like document or graph databases. Discuss when to choose one over the other based on query patterns, data structure, and scalability needs.
Data Governance and Security Standards
What to coverHow do you establish and maintain data governance and security best practices in a large organization? Address the use of IAM roles, data encryption, and ensuring that data solutions remain auditable and compliant with organizational standards.
Integrating Generative AI Tools in Development
What to coverProvide examples of how you have used AI-powered IDEs or agentic coding assistants to improve your own productivity or code quality. Suggestion: Be prepared to discuss specific instances where these tools helped you debug complex logic or generate boilerplate code efficiently.
Handling Real-time Data Streams
What to coverExplain how you would architect a solution using Kinesis or Firehose to ingest and process real-time data. Discuss how to handle backpressure, ensure data consistency, and integrate these streams into downstream analytical platforms.
Collaborating with Data Scientists and Economists
What to coverDescribe your process for translating research requirements from Data Scientists into scalable production data products. Focus on how you manage expectations, define data contracts, and iterate on infrastructure to support experimental models.
Behavioural practice
Driving Operational Excellence
What to coverDescribe a situation where you identified a significant system-wide performance issue and took the lead to resolve it. Use the STAR method to explain the problem, the specific actions you took to diagnose and fix it, and the measurable impact on system reliability or performance.
Navigating Cross-Functional Collaboration
What to coverTell me about a time you had to collaborate with non-engineering stakeholders to deliver a complex data solution. Explain how you communicated technical constraints and trade-offs to ensure the final product met both business objectives and technical standards.
Adapting to New Technologies
What to coverGive an example of a time you had to quickly learn and implement a new technology or tool to solve a project challenge. Detail your learning process and how you successfully integrated the new tool into your existing workflow.
Managing Technical Debt vs. Innovation
What to coverDescribe a scenario where you had to balance the need for rapid innovation with the necessity of maintaining high-quality, maintainable code. How did you decide what to prioritize and how did you communicate this to your team or leadership?