AWS DE - Associate Manager

As an AWS Data Engineer - Associate Manager, your primary responsibilities will include designing and maintaining data pipelines using AWS technologies such as Glue, Redshift, and EMR. You'll also engage in architectural discussions and help optimize the data integration platform performance. Additionally, you'll be involved in supporting data architecture and governance to enhance the organization’s data capabilities.

Candidates for the AWS Data Engineer - Associate Manager position should have a BE/BTech or MCA degree along with 7 to 10 years of experience in relevant skills such as AWS data services, SQL, and PySpark. Hands-on experience with AWS technologies, data migration, and big data on the AWS ecosystem are essential for success in this role.

Key technical skills for the AWS Data Engineer - Associate Manager role include proficiency in AWS data services like Glue, Redshift, EMR, and Kinesis. A strong understanding of SQL, PySpark, and experience with AWS managed services are necessary. Familiarity with data migration tools, AWS CLI, and scripting languages will also be beneficial.

Client interaction is a significant aspect of the AWS Data Engineer - Associate Manager role. You will be expected to engage with clients during architectural discussions and ensure the solutions you develop meet their unique requirements and expectations, thus fostering trust and collaboration.

Yes, AWS certifications are advantageous for the AWS Data Engineer - Associate Manager position. While not mandatory, they demonstrate your commitment to mastering AWS services and can enhance your candidacy by showcasing your expertise and proficiency in cloud technologies.

When answering this question, discuss your hands-on experience with relevant AWS services such as Glue, Redshift, and EMR. Highlight specific projects where you successfully designed data pipelines or optimized data integration processes, emphasizing the impact of these projects on previous employers.

Discuss your methodology for optimizing data pipelines, such as monitoring performance metrics, employing efficient data transformation techniques, or leveraging AWS tools like Athena for performance enhancement. Provide a specific example of how your optimization efforts improved data handling capabilities.

When answering, clearly outline the challenge, the steps you took to analyze the issue, and the solutions you implemented. Use specific metrics or outcomes to illustrate the effectiveness of your solution, showing your problem-solving skills and technical acumen.

Discuss your experience with AWS Data Migration Service and strategies like incremental data migration or data synchronization to ensure successful and efficient data transition. Detail any common challenges you've faced and how you overcame them.

Talk about your approach to establishing data governance practices, such as data validation, data cleansing, and documentation. Mention any tools you've used or frameworks you've implemented to maintain high data quality and compliance with governance standards.

Focus on your practical experience with ETL processes in AWS, discussing specific tools like Glue for automation, and how you ensure data integrity and performance. Include examples of ETL processes you've built and how they benefited the organization.

Illustrate your communication and teamwork skills by describing experiences where you successfully collaborated with data scientists, software developers, or stakeholders to align goals and deliver successful projects, showcasing your role in facilitating this collaboration.

List the monitoring tools you've utilized, such as AWS CloudWatch, and describe how you've employed them to track application health and performance. Provide an example of how you identified and resolved a critical issue using monitoring data.

Discuss your understanding of data modeling, including designing schemas for data warehouses in Redshift or databases in DynamoDB. Highlight how you ensure your designs support analytical needs and scalability.

When answering, explain how serverless architecture can enhance efficiency and scalability in data processing workflows. Provide examples of how you've utilized AWS Lambda to build event-driven data pipelines or automate tasks effectively.