Technical practice
Implementing Medallion Architecture in Databricks
What to coverExplain how you would structure data across Bronze, Silver, and Gold layers. Discuss the specific transformations and quality checks you would apply at each stage to ensure data reliability for downstream analytics.
Data Governance with Unity Catalog
What to coverDescribe how you use Unity Catalog to manage data discovery, lineage, and access control. Explain the benefits of centralized governance in a multi-user environment compared to traditional siloed approaches.
ETL Pipeline Design and Optimization
What to coverDiscuss your approach to designing scalable ETL pipelines using Databricks Workflows. Address how you handle incremental data loading, error handling, and monitoring to ensure data quality.
Integrating SaaS Data with AWS and Databricks
What to coverExplain your strategy for extracting data from external SaaS platforms (like Salesforce) into an AWS-based data lake. Detail how you handle schema evolution and API rate limiting during the ingestion process.
Machine Learning Pipeline Implementation
What to coverDescribe your experience building basic ML pipelines within Databricks. Focus on how you prepare features, manage model training, and ensure the resulting insights are accessible for business intelligence.
AWS Storage and Query Optimization
What to coverHow do you choose between S3, RDS, and Athena for different data storage and retrieval needs? Explain how you optimize query performance in Athena for large datasets stored in S3.
Python and SQL for Data Transformation
What to coverProvide an example of a complex data transformation task you handled using Python or SQL. Explain why you chose the specific approach and how you ensured the code was performant and maintainable.
Collaborative Analytics with Jupyter Notebooks
What to coverHow do you manage version control and collaboration when working with Jupyter notebooks in a team environment? Discuss best practices for notebook modularization and documentation.
Behavioural practice
Collaborating with Non-Technical Stakeholders
What to coverDescribe a time you had to translate complex data requirements from a stakeholder into a technical solution. How did you ensure the final output met their business needs?
Handling Data Quality Issues
What to coverTell me about a situation where you discovered a significant data quality issue in a production pipeline. What steps did you take to identify the root cause, fix the data, and prevent recurrence?
Working Independently on Projects
What to coverThis role requires working independently on data pipeline projects. Share an example of a project where you had to manage your own timeline and technical decisions with minimal supervision.
Adapting to New Technologies or Frameworks
What to coverDescribe a time you had to quickly learn and implement a new tool or framework (e.g., a specific AWS service or Databricks feature) to solve a project challenge. How did you approach the learning process?