Technical practice
Designing Scalable Lakehouse Architectures
What to coverExplain your approach to designing a lakehouse architecture on GCP that balances performance and cost. Discuss how you handle data ingestion, storage layers, and the integration of compute engines to support both batch and real-time analytics.
Implementing Robust MLOps Pipelines
What to coverDescribe the components of a production-grade MLOps pipeline you have built. Focus on how you manage model versioning, automated testing, and deployment strategies to ensure reliability and reproducibility.
Monitoring and Drift Detection
What to coverHow do you implement automated monitoring for machine learning models in production? Detail your strategy for detecting data drift, concept drift, and performance degradation, and how you trigger automated retraining or alerting.
Data Governance and Lineage
What to coverWhat frameworks do you use to enforce data governance and lineage in a large-scale enterprise environment? Explain how you ensure data quality, freshness, and compliance while maintaining visibility across complex pipeline dependencies.
Orchestration at Scale
What to coverCompare different orchestration tools for managing complex data and ML workflows. Discuss how you handle task dependencies, retries, and failure recovery in a distributed computing environment.
CI/CD for Machine Learning
What to coverExplain the challenges of implementing CI/CD for ML systems compared to traditional software. Describe how you integrate model validation and automated testing into the deployment lifecycle.
Optimizing Distributed Computing Jobs
What to coverWhen a data pipeline fails to meet SLA due to performance bottlenecks, what is your diagnostic process? Discuss techniques for optimizing distributed jobs on GCP, such as partitioning strategies, resource allocation, and query tuning.
Python and SQL for Data Engineering
What to coverProvide an example of how you have used Python and SQL to solve a complex data transformation problem. Focus on code modularity, performance efficiency, and maintainability.
Behavioural practice
Managing Global Stakeholder Expectations
What to coverDescribe a time you had to translate strategic business needs into a technical roadmap for a global team. How did you handle conflicting priorities across different time zones and ensure alignment with business outcomes?
The Player-Coach Approach
What to coverGive an example of how you have balanced hands-on technical contributions, such as code reviews or architecture design, with your management responsibilities. How do you decide when to step in versus when to delegate?
Mentoring and Team Growth
What to coverDescribe a situation where you identified a skill gap in your team. What steps did you take to mentor the engineers and foster a culture of technical rigor and autonomy?
Driving Cultural Change
What to coverTell me about a time you introduced a new engineering practice or tool, such as a new MLOps framework, to your organization. How did you overcome resistance and ensure successful adoption?