I applied for Senior Data Engineer position at CVS Health through their online job portal. After submitting my application, I received an email confirming the receipt of my application. After 3 weeks, I was contacted by a Talent Acquisition Specialist to schedule an initial phone screening in which she explained about the role and asked me questions about my current work. The phone screening went well, and I was invited to participate in a series of online interviews.
Round 1: Technical Interview with Lead Data Engineer (Online):
We began by discussing the job responsibilities and the importance of data engineering in generating insights and addressing reporting needs.
- Question: Can you describe your experience in developing large-scale data structures and pipelines to organize, collect, and standardize data?
- Question: How have you designed database systems and managed ETL processes for both real-time and offline analytics?
- Question: How have you used Hadoop architecture and HDFS commands to build data pipelines?
I explained my previous projects involving the design and implementation of data pipelines using various technologies, including Apache Spark and Apache Kafka. I highlighted how these pipelines enabled the ingestion of vast amounts of data from different sources, transforming and preparing it for downstream analytics.
I elaborated on my experience with Hadoop ecosystem tools such as Hive and Pig, explaining how they were instrumental in processing and storing data in HDFS. I discussed the optimization of queries and data retrieval techniques to improve performance.
Tip: Focus on sharing details about large-scale data engineering projects you have undertaken in the past. Emphasize your ability to handle massive data volumes and the techniques you used to optimize data pipelines for performance and scalability.
Tip: Discuss your experience in designing data models and ETL processes for both real-time streaming and batch processing. Mention the technologies you used, such as Apache Kafka for real-time streaming and Apache Spark for batch processing.
Round 2: Technical Interview with Data Science Lead (Online):
The second round of interview was with the Data Science Lead. The focus was on collaboration and integrating algorithms and models into automated processes.
- Question: How have you collaborated with Data Science teams in the past to integrate algorithms into data engineering processes?
I shared examples of projects where I had closely worked with Data Scientists to deploy their machine learning models into real-time and batch processing systems. I emphasized the need for seamless integration between data engineering and data science to deliver actionable insights.
Tip: Highlight projects where you worked closely with data scientists to implement machine learning models into automated data pipelines. Discuss how you ensured the models’ accuracy, scalability, and ease of maintenance within the data engineering systems.
Round 3: Technical Interview on Programming Skills (Online):
Next, I had a technical interview that assessed my programming skills, particularly in Python and Java.
- Question: Can you discuss your experience in using Python and Java to build data pipelines and dynamic systems?
I explained my proficiency in Python and how it allowed me to build scalable and robust data pipelines. I also mentioned my experience using Java for building high-performance applications. I provided examples of projects where I leveraged these languages to handle complex data processing scenarios.
Tip: Describe specific projects where you have used Python or Java to design and develop data pipelines. Mention how you leveraged the strengths of each language for specific data processing tasks.
Round 4: Technical Interview on Data Modeling (Online):
The following interview focused on data modeling and building data marts to support Data Science and other internal customers.
- Question: How do you approach building data marts and data models to support analytical needs?
I described my approach to understanding business requirements and translating them into effective data models. I highlighted the importance of data quality and adherence to data accessibility standards. I also shared instances where I integrated data from diverse sources to provide comprehensive insights.
Tip: Discuss your methodology for gathering requirements from stakeholders and designing data models that cater to their specific analytics needs. Highlight the importance of data quality and adherence to accessibility standards in your data models.
Round 5: Technical Interview on Tools and Solutions (Online):
The final interview involved discussing my ability to experiment with different tools and identify optimal solutions for specific model/use cases.
- Question: Can you provide examples of experimenting with tools to determine the best solution for a particular data engineering challenge?
I discussed a recent project where I experimented with various data processing frameworks, including Apache Spark and Apache Flink, to determine the best fit for a real-time data streaming use case. I explained the considerations and criteria used to make the final decision.
Tip: Share a situation where you had multiple options for data engineering tools and how you systematically evaluated each one’s strengths and weaknesses to arrive at the best solution for the specific use case.
Conclusion:
The entire interview process at CVS Health was challenging. There were too many rounds. Some interviewers were engaging and supportive while others were daunting and expected me to tell all minute details of my current work.