Data Engineer

Summary

• Design, develop, and maintain optimal data pipeline architectures.
• Collaborate with data scientists and analysts to transform data into formats suitable for analytics.
• Ensure high availability and performance of data infrastructure, including databases, data lakes, and big data platforms.
• Stay updated with the latest data storage and retrieval methodologies.
• Implement automation and data validation processes to increase efficiency and data integrity.

Key Skills

• Proficiency in big data tools like Hadoop, Spark, and Kafka.
• Experience with relational SQL and NoSQL databases, including Postgres, Cassandra, and MongoDB.
• Ability to design and implement ETL (Extract, Transform, Load) processes.
• Knowledge of data warehousing solutions such as Amazon Redshift or Google BigQuery.
• Familiarity with data pipeline and workflow management tools like Apache NiFi and Apache Airflow.

Standard Industry Training for Data Engineers

• Google Cloud Professional Data Engineer Certification
• Azure Data Engineer Associate Certification
• AWS Certified Big Data – Specialty

Interview Questions:

1. How do you ensure that the data in a pipeline is accurate and reliable?
2. Describe a situation where you had to handle a large influx of data in real-time. How did you manage it?
3. What are the key considerations when migrating data from a traditional relational database to a big data platform?
4. How do you handle schema changes or evolving data structures in your pipelines?
5. Discuss a challenging problem you faced in data integration and how you solved it.