Technical practice
Designing ETL/ELT Pipelines in Snowflake
What to coverExplain your approach to designing scalable ETL/ELT pipelines using Snowflake and dbt. Discuss how you handle incremental loading, data quality checks, and performance optimization within the Snowflake environment.
Integrating AWS SageMaker and Bedrock
What to coverDescribe how you would architect a data pipeline that feeds data into AWS SageMaker for training or AWS Bedrock for generative AI applications. Focus on data preparation, feature store integration, and ensuring secure access to data sources like S3.
Implementing RAG Pipelines
What to coverDiscuss the technical components required to build a Retrieval-Augmented Generation (RAG) pipeline. Address how you manage vector databases, handle document ingestion, and ensure the relevance of retrieved context for LLMs.
MLOps and Model Monitoring
What to coverExplain your strategy for MLOps, specifically regarding model deployment and continuous monitoring. Mention how you would use tools like Fiddler Exchange or native AWS services to detect model drift and ensure operational excellence.
Data Governance and Security
What to coverHow do you ensure data governance, security, and regulatory compliance when handling sensitive data, such as healthcare or clinical information? Discuss your experience with access controls, encryption, and audit logging in cloud environments.
Troubleshooting Production Data Issues
What to coverDescribe your methodology for troubleshooting complex production issues in a distributed data environment. Focus on how you use logging, monitoring tools like CloudWatch, and root cause analysis to minimize downtime.
Infrastructure as Code (IaC) with Terraform
What to coverExplain how you use Terraform to manage cloud infrastructure for data platforms. Discuss the benefits of using IaC for maintaining consistency, scalability, and reproducibility across development and production environments.
Dimensional Modeling for AI/ML
What to coverHow does your approach to dimensional modeling change when the downstream consumer is an AI/ML model rather than a traditional BI dashboard? Discuss the trade-offs between normalized and denormalized structures for model training efficiency.
Behavioural practice
Cross-functional Collaboration
What to coverDescribe a situation where you had to bridge the gap between Data Science, ML Engineering, and DevOps teams. How did you align technical requirements and ensure the final solution met the needs of all stakeholders?
Translating Business Requirements
What to coverProvide an example of a time you translated complex business or AI/ML requirements into a concrete technical architecture. What was the challenge, and how did you ensure the resulting solution was scalable and reliable?
Handling Conflicting Priorities
What to coverTell me about a time you had to manage competing priorities between maintaining existing data pipelines and implementing new AI/ML features. How did you decide what to focus on, and how did you communicate this to your stakeholders?
Driving Operational Excellence
What to coverGive an example of an initiative you led to improve automation or reliability in a data platform. What specific metrics did you aim to improve, and what was the outcome of your efforts?