AWS DE - Associate Director

As an AWS Data Engineer at our company, your main responsibilities will include creating and supporting data pipelines built on AWS technologies like Glue, Redshift, EMR, Kinesis, and Athena. You will also participate in architectural discussions to ensure that our solutions meet customer requirements and seamlessly integrate with existing data applications. Performance optimization and expanding our data architecture and governance capabilities will be key parts of your role, contributing significantly to our overall data strategy.

To apply for the Associate Director AWS Data Engineer role at our company, you need a BE/BTech/MCA degree, along with 12 to 15 years of extensive experience in relevant technologies. Expertise in AWS data services and familiarity with big data technologies, SQL, and programming languages such as Python or PySpark are crucial. Having AWS certifications is a bonus and can enhance your candidacy.

Success as an AWS Data Engineer at our company requires a strong technical foundation in AWS services, including experience with AWS Glue, Redshift, and data migration tools. You should have hands-on experience in building and managing data pipelines, as well as a solid understanding of data modeling and ETL processes on AWS. Team management abilities and strong communication skills are also essential for collaborating effectively and engaging with clients.

For the AWS Data Engineer Associate Director position at our company, proficiency in Python or PySpark, as well as familiarity with any object-oriented or functional scripting languages, is essential. These skills will enable you to effectively create data pipelines and perform data manipulation tasks within the AWS environment.

The team culture for AWS Data Engineers at our company is collaborative and innovative, with a strong emphasis on learning and growth. We believe in building an environment where team members can share ideas, participate in architectural discussions, and work together on challenging projects. Your contributions will be valued, and we encourage personal development, ensuring you stay at the forefront of technological advancements in data engineering.

In answering this question, focus on specific projects where you utilized AWS Glue for ETL processes. Describe how you automated data workflows and the benefits that Glue brought to your data pipeline. Highlight any challenges you faced and how you overcame them using Glue features.

Discuss the importance of monitoring data volume and performance metrics. Provide examples of tools you've used, such as AWS CloudWatch, to continuously optimize and scale your data pipelines efficiently.

Explain your approach to handling data migrations, including the tools (like AWS DMS) you utilize and the steps taken to ensure data integrity during the migration process. Describe a specific migration project you've managed, emphasizing the challenges and outcomes.

Emphasize the need for clear, effective communication among team members and stakeholders. Provide examples of scenarios where communication has helped clarify project requirements or resolve issues quickly.

Talk about your experience with implementing data governance frameworks in previous roles. Explain the importance of documentation, access controls, and data lineage tracking in maintaining compliance throughout your data processes.

Provide a specific example that showcases your problem-solving skills and innovation. Describe the project's challenges, how you devised a solution, and the lessons learned from the experience.

Discuss the projects where you have set up and managed Redshift clusters. Highlight your understanding of creating schemas, writing complex queries, and optimizing Redshift for performance.

Outline your strategy for understanding project requirements, selecting appropriate AWS services, and designing a scalable architecture. Stress the importance of documentation and validation with stakeholders in your design process.

Mention specific tools you have experience with, such as AWS Glue, AWS Lambda, or Apache Airflow. Explain why you choose these tools based on project needs, efficiency, and ease of integration.

Discuss your commitment to continuous learning, such as attending AWS training sessions, obtaining certifications, participating in online courses, or engaging with the AWS community through meetups and forums.