Data Engineer

Priority reflects how strongly this topic is emphasised in the job listing, not whether it will be asked.

1. 1Can you describe a project where you designed and implemented a data pipeline using dbt or a similar SQL-based transformation framework?High

   Why they may ask

   To assess your practical experience with modern data transformation tools and your approach to building robust pipelines.

   Prepare to discuss

   • Clearly outline the project context and objectives.
   • Describe your specific responsibilities and the technologies used.
   • Explain challenges faced and how you addressed them.
   • Highlight outcomes such as improved data quality, performance, or maintainability.

   Tip: Use the STAR method (Situation, Task, Action, Result) to structure your answer.

2. 2How have you leveraged cloud data platforms like Databricks or Microsoft Fabric in your previous roles?High

   Why they may ask

   To evaluate your familiarity with cloud-based data engineering environments and your ability to utilize their features.

   Prepare to discuss

   • Mention specific platforms and features you used.
   • Discuss how you managed data storage, processing, and security.
   • Share any optimizations or best practices you implemented.

   Tip: Be specific about your hands-on contributions rather than general platform knowledge.

3. 3What is your approach to building layered or medallion data models, and why are they important?High

   Why they may ask

   To determine your understanding of modern data architecture principles and your ability to apply them.

   Prepare to discuss

   • Explain the concept of layered/medallion architecture.
   • Describe how you have implemented it in practice.
   • Discuss benefits such as scalability, maintainability, and data quality.

   Tip: Use a real-world example to illustrate your approach.

4. 4Describe your experience with workflow orchestration tools in managing data pipelines.Medium

   Why they may ask

   To assess your ability to automate, schedule, and monitor data workflows effectively.

   Prepare to discuss

   • Name the orchestration tools you have used (e.g., Airflow, Prefect).
   • Explain how you set up and managed workflows.
   • Discuss how you handled failures and ensured reliability.

   Tip: Highlight any improvements you made to existing workflows.

5. 5Have you migrated legacy ETL or reporting logic to modern frameworks? What challenges did you face and how did you validate the outputs?Medium

   Why they may ask

   To gauge your experience with modernization projects and your attention to data quality and validation.

   Prepare to discuss

   • Describe the legacy system and the target modern framework.
   • Explain the migration process and key challenges.
   • Detail your validation and testing approach to ensure accuracy.

   Tip: Emphasize your problem-solving and quality assurance skills.

6. 6What strategies do you use to build data ingestion pipelines from APIs or databases, especially for incremental or CDC processes?Medium

   Why they may ask

   To understand your approach to efficient and reliable data ingestion, particularly for real-time or near-real-time data.

   Prepare to discuss

   • Discuss tools and techniques for API/database ingestion.
   • Explain how you handle incremental loads or change data capture.
   • Share how you ensure data consistency and performance.

   Tip: Mention any monitoring or alerting mechanisms you implemented.

7. 7How do you ensure the data models you build are suitable for downstream BI and visualization platforms?Lower

   Why they may ask

   To assess your understanding of the end-to-end data pipeline and your ability to support analytics use cases.

   Prepare to discuss

   • Describe collaboration with BI teams or stakeholders.
   • Explain how you design models for usability and performance.
   • Share examples of feedback or improvements made.

   Tip: Highlight your communication and cross-functional skills.