Data Engineer (Streaming)

- Can you explain the differences between batch processing and stream processing? When would you use one over the other?
- Describe your experience with real-time data processing frameworks such as Apache Kafka, Apache Flink, or Apache Storm.
- How would you go about designing a data pipeline for processing streaming data from multiple sources in real time?
- What challenges have you faced with data latency and throughput in streaming applications and how did you address them?
- How do you ensure data accuracy and consistency when dealing with real-time data streams?
- Can you provide an example of a time when you optimized the performance of a streaming data pipeline? What tools and techniques did you use?
- How do you handle event-time processing versus processing-time in stream processing frameworks?
- What strategies do you use to handle fault tolerance and data recovery in stream processing systems?
- How do you manage schema changes in a streaming data environment?
- Discuss your experience with any data serialization formats like Avro, Parquet, or Thrift in the context of streaming data.

- Describe a complex data pipeline you designed for real-time processing. What challenges did you face, and how did you solve them?
- Explain a situation where you had to optimize a streaming process. What strategies did you employ and what was the outcome?
- How do you handle sudden spikes in data volume in a streaming system? Provide an example from your experience.
- Describe a time when you had to integrate a new data source into an existing streaming infrastructure. What was your approach, and what innovative solutions did you implement?
- Have you ever encountered data quality issues in a streaming environment? How did you identify and resolve them?
- Explain a scenario where you automated a repetitive task within a data stream. What tools and methods did you use?
- Tell me about a time you improved the performance of a data stream. What metrics did you use to measure success, and what changes had the most significant impact?
- Describe how you approached solving a resource bottleneck in streaming data architecture. What innovative approaches did you use?
- Can you provide an example of how you implemented fault-tolerance in a streaming pipeline? What specific challenges did you overcome?
- Discuss a project where you employed new technologies or frameworks in data streaming. How did you assess their suitability and what were the results of your innovation?

- Can you describe an instance where you had to explain a complex data engineering concept to a non-technical team member? How did you ensure they understood?
- How do you approach collaborating with cross-functional teams, such as data scientists and product managers, on streaming data projects?
- Can you provide an example of how you have handled a disagreement or conflict within a team setting? What steps did you take to resolve it?
- Describe a time when you had to gather requirements for a streaming data project from multiple stakeholders. How did you ensure all their needs were met?
- Have you ever had to mentor or train a junior team member on streaming data techniques? How did you facilitate their learning?
- How do you ensure effective and clear communication when working remotely with team members on a streaming data pipeline?
- Can you discuss a project where team collaboration was critical to the project's success? What was your role, and how did you contribute to team synergy?
- Describe a situation where you had to deliver bad news about a project, like missing a deadline or encountering a significant issue. How did you communicate this to the team or stakeholders?
- How do you give and receive feedback in a team setting, particularly in the context of improving streaming data workflows?
- Can you talk about a time when you had to coordinate with team members across different time zones for a streaming data project? How did you manage communication and workflow?

- Can you describe a project where you had to manage resource constraints and how you handled it?
- How do you prioritize tasks and resources when working on multiple projects simultaneously?
- Can you provide an example of how you estimated the time and resources required for a data streaming project?
- Describe a time when you had to adjust project timelines due to resource limitations.
- What strategies do you use to ensure that your data engineering projects stay on budget?
- How do you allocate resources when planning for data ingestion and streaming pipelines?
- Can you share an experience where you had to manage resource dependencies within a data streaming project?
- How do you handle unexpected changes in resource availability during a project?
- Have you ever had to reassign resources mid-project? If so, how did you manage the transition?
- What tools or methodologies do you use to track resource utilization and project progress in data engineering projects?

- How do you ensure that the data you process and manage complies with relevant data privacy laws and regulations (e.g., GDPR, CCPA)?
- Can you describe a time when you identified a potential ethical issue in a data project? How did you handle it?
- How do you balance the need for data availability with protecting sensitive information?
- What steps would you take if you discovered that data being used for a streaming application originated from an unethical source or was obtained without proper consent?
- How do you ensure transparency and accountability in your data engineering processes?
- How do you handle situations where there is a conflict between business objectives and ethical considerations in data usage?
- What approaches do you take to ensure data integrity and prevent manipulation or tampering in streaming data pipelines?
- Can you discuss your experience with implementing data governance policies in a streaming data environment?
- How do you educate and enforce ethical data practices within your team or organization?
- How do you stay updated on the latest ethical standards and legal requirements related to data engineering and streaming technologies?

- Can you describe a time when you had to learn a new technology or tool quickly for a project? How did you approach it?
- How do you stay updated with the latest trends and advancements in data engineering, particularly in streaming technologies?
- Can you give an example of how you’ve adapted to a significant change in a project or workplace, especially in handling real-time data?
- What strategies do you use to keep improving your skills and knowledge in data streaming?
- Describe a situation where your initial solution didn't work as expected. How did you handle it and what did you learn from it?
- How do you incorporate feedback and performance reviews into your professional development, particularly in technical roles involving streaming data?
- Have you ever taken the initiative to suggest a new tool or process for handling streaming data? What was the outcome?
- What are some of the biggest challenges you’ve faced in the rapidly evolving field of data streaming, and how have you overcome them?
- How do you balance learning new skills with applying existing knowledge to ensure continuous growth in your role?
- Can you describe a project where you had to pivot the technical approach due to new information or changing requirements? How did you manage the transition?